General Setup


Create a new analysis directory...
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[1] "/Users/swvanderlaan/git/CirculatoryHealth/AE_20211201_YAW_SWVANDERLAAN_HDAC9"
 [1] "_archived"                                     "1. AEDB.CEA.baseline.nb.html"                  "1. AEDB.CEA.baseline.Rmd"                     
 [4] "2. SNP_analyses.Rmd"                           "20220319.HDAC9.AEDB.CEA.baseline.RData"        "20220319.HDAC9.AESCRNA.results.RData"         
 [7] "20220319.HDAC9.bulkRNAseq.main_analysis.RData" "20220319.HDAC9.bulkRNAseq.preparation.RData"   "3.1 bulkRNAseq.preparation.nb.html"           
[10] "3.1 bulkRNAseq.preparation.Rmd"                "3.2 bulkRNAseq.main_analysis.nb.html"          "3.2 bulkRNAseq.main_analysis.Rmd"             
[13] "3.3 bulkRNAseq.additional_figures.Rmd"         "4. scRNAseq.nb.html"                           "4. scRNAseq.Rmd"                              
[16] "AE_20211201_YAW_SWVANDERLAAN_HDAC9.Rproj"      "AnalysisPlan"                                  "HDAC9"                                        
[19] "images"                                        "LICENSE"                                       "README.html"                                  
[22] "README.md"                                     "references.bib"                                "renv"                                         
[25] "renv.lock"                                     "scripts"                                       "SNP"                                          
[28] "targets"                                      
source(paste0(PROJECT_loc, "/scripts/functions.R"))
install.packages.auto("pander")
install.packages.auto("readr")
install.packages.auto("optparse")
install.packages.auto("tools")
install.packages.auto("dplyr")
install.packages.auto("tidyr")
install.packages.auto("naniar")

# To get 'data.table' with 'fwrite' to be able to directly write gzipped-files
# Ref: https://stackoverflow.com/questions/42788401/is-possible-to-use-fwrite-from-data-table-with-gzfile
# install.packages("data.table", repos = "https://Rdatatable.gitlab.io/data.table")
library(data.table)

install.packages.auto("tidyverse")
install.packages.auto("knitr")
install.packages.auto("DT")
install.packages.auto("eeptools")

install.packages.auto("openxlsx")

install.packages.auto("haven")
install.packages.auto("tableone")
install.packages.auto("sjPlot")

install.packages.auto("BlandAltmanLeh")

# Install the devtools package from Hadley Wickham
install.packages.auto('devtools')

# for plotting
install.packages.auto("pheatmap")
install.packages.auto("forestplot")
install.packages.auto("ggplot2")

install.packages.auto("ggpubr")

install.packages.auto("UpSetR")

devtools::install_github("thomasp85/patchwork")
Using github PAT from envvar GITHUB_PAT
Skipping install of 'patchwork' from a github remote, the SHA1 (79223d30) has not changed since last install.
  Use `force = TRUE` to force installation
install.packages.auto("GGally")
install.packages.auto("ggcorrplot")

# for Seurat etc
install.packages.auto("GenomicFeatures")
install.packages.auto("GenomicRanges")
install.packages.auto("SummarizedExperiment")
install.packages.auto("DESeq2")
install.packages.auto("org.Hs.eg.db")
install.packages.auto("mygene")
install.packages.auto("TxDb.Hsapiens.UCSC.hg19.knownGene")
install.packages.auto("org.Hs.eg.db")
install.packages.auto("AnnotationDbi")
install.packages.auto("EnsDb.Hsapiens.v86")
Loading required package: EnsDb.Hsapiens.v86
Loading required package: ensembldb

Attaching package: 'ensembldb'

The following object is masked from 'package:openxlsx':

    addFilter

The following object is masked from 'package:dplyr':

    filter

The following object is masked from 'package:stats':

    filter
install.packages.auto("EnhancedVolcano")

Today = format(as.Date(as.POSIXlt(Sys.time())), "%Y%m%d")
Today.Report = format(as.Date(as.POSIXlt(Sys.time())), "%A, %B %d, %Y")

### UtrechtScienceParkColoursScheme
###
### WebsitetoconvertHEXtoRGB:http://hex.colorrrs.com.
### Forsomefunctionsyoushoulddividethesenumbersby255.
###
### No. Color                 HEX   (RGB)                                     CHR         MAF/INFO
###---------------------------------------------------------------------------------------
### 1     yellow                #FBB820 (251,184,32)                      =>    1       or 1.0>INFO
### 2     gold                #F59D10 (245,157,16)                    =>    2       
### 3     salmon                #E55738 (229,87,56)                   =>    3       or 0.05<MAF<0.2 or 0.4<INFO<0.6
### 4     darkpink          #DB003F ((219,0,63)                   =>    4       
### 5     lightpink         #E35493 (227,84,147)                      =>    5       or 0.8<INFO<1.0
### 6     pink                #D5267B (213,38,123)                    =>    6       
### 7     hardpink          #CC0071 (204,0,113)                   =>    7       
### 8     lightpurple       #A8448A (168,68,138)                      =>    8       
### 9     purple                #9A3480 (154,52,128)                      =>    9       
### 10  lavendel            #8D5B9A (141,91,154)                      =>    10      
### 11  bluepurple        #705296 (112,82,150)                    =>    11      
### 12  purpleblue        #686AA9 (104,106,169)               =>    12      
### 13  lightpurpleblue #6173AD (97,115,173/101,120,180)    =>  13      
### 14  seablue             #4C81BF (76,129,191)                      =>    14      
### 15  skyblue             #2F8BC9 (47,139,201)                      =>    15      
### 16  azurblue            #1290D9 (18,144,217)                      =>    16      or 0.01<MAF<0.05 or 0.2<INFO<0.4
### 17  lightazurblue     #1396D8 (19,150,216)                    =>    17      
### 18  greenblue           #15A6C1 (21,166,193)                      =>    18      
### 19  seaweedgreen      #5EB17F (94,177,127)                    =>    19      
### 20  yellowgreen       #86B833 (134,184,51)                    =>    20      
### 21  lightmossgreen  #C5D220 (197,210,32)                      =>    21      
### 22  mossgreen           #9FC228 (159,194,40)                      =>    22      or MAF>0.20 or 0.6<INFO<0.8
### 23  lightgreen      #78B113 (120,177,19)                      =>    23/X
### 24  green                 #49A01D (73,160,29)                     =>    24/Y
### 25  grey                  #595A5C (89,90,92)                        =>  25/XY   or MAF<0.01 or 0.0<INFO<0.2
### 26  lightgrey           #A2A3A4 (162,163,164)                 =>    26/MT
###
### ADDITIONAL COLORS
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### 28  verylightgrey   #ECECEC"
### 29  white           #FFFFFF
### 30  black           #000000
###----------------------------------------------------------------------------------------------

uithof_color = c("#FBB820","#F59D10","#E55738","#DB003F","#E35493","#D5267B",
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                 "#595A5C","#A2A3A4", "#D7D8D7", "#ECECEC", "#FFFFFF", "#000000")

uithof_color_legend = c("#FBB820", "#F59D10", "#E55738", "#DB003F", "#E35493",
                        "#D5267B", "#CC0071", "#A8448A", "#9A3480", "#8D5B9A",
                        "#705296", "#686AA9", "#6173AD", "#4C81BF", "#2F8BC9",
                        "#1290D9", "#1396D8", "#15A6C1", "#5EB17F", "#86B833",
                        "#C5D220", "#9FC228", "#78B113", "#49A01D", "#595A5C",
                        "#A2A3A4", "#D7D8D7", "#ECECEC", "#FFFFFF", "#000000")
### ----------------------------------------------------------------------------

Main analyses

Here we perform the main analyses. We first load the prepared RNAseq data, and extract only the relevant genes.

Targets

Here we obtain data from the HDAC9 in plaques.

library(openxlsx)

gene_list_df <- read.xlsx(paste0(PROJECT_loc, "/targets/Genes.xlsx"), sheet = "Genes")

target_genes <- unlist(gene_list_df$Gene)
target_genes
[1] "HDAC9"  "TWIST1" "IL6"    "IL1B"  

Loading data

We simply load the previously saved RDS-file and extract the clinical and RNAseq data from that.

AEDB.CEA <- readRDS(file = paste0(OUT_loc, "/20220319.",TRAIT_OF_INTEREST,".AEDB.CEA.RDS"))
AEDB.CEA$STUDY_NUMBER <- paste0("ae", AEDB.CEA$STUDY_NUMBER)
head(AEDB.CEA$STUDY_NUMBER)
[1] "ae3"  "ae7"  "ae9"  "ae12" "ae13" "ae15"
AERNASE <- readRDS(file = paste0(OUT_loc, "/20220319.AERNA.CEA.623pts.SE.after_qc.IC_academic.RDS"))
AERNASE.clin <- as_tibble(colData(AERNASE))

AERNASE.hdac9 <- subset(AERNASE, subset = (rowRanges(AERNASE)$symbol %in% target_genes))

row.names(AERNASE.hdac9) <- rowData(AERNASE.hdac9)$symbol
temp <- as_tibble(t(assay(AERNASE.hdac9)), rownames = "STUDY_NUMBER")

AERNASE.clin.hdac9 <- as.data.frame(merge(AERNASE.clin, temp, by.x = "STUDY_NUMBER", by.y = "STUDY_NUMBER", sort = TRUE))
rm(temp)

Getting some summary statistics.

cat("Average expression of a random selection of 1000 genes.\n")
Average expression of a random selection of 1000 genes.
set.seed(141619)
mean(rowMeans(as_tibble(assay(AERNASE)) %>% dplyr::sample_n(1000)))
[1] 12.54526
cat("\nAverage expression of target genes.\n")

Average expression of target genes.
rowMeans(assay(AERNASE.hdac9))
     HDAC9     TWIST1       IL1B        IL6 
28.3226324  5.5553772  5.6950241  0.4109149 
cat("\nGene information.\n")

Gene information.
rowRanges(AERNASE.hdac9)
GRanges object with 4 ranges and 3 metadata columns:
         seqnames              ranges strand |      feature_id      symbol GENEBIOTYPE_EnsDb86
            <Rle>           <IRanges>  <Rle> |     <character> <character>         <character>
   HDAC9        7   18086949-19002416      + | ENSG00000048052       HDAC9      protein_coding
  TWIST1        7   19020991-19117636      - | ENSG00000122691      TWIST1      protein_coding
    IL1B        2 112829751-112836816      - | ENSG00000125538        IL1B      protein_coding
     IL6        7   22725884-22732002      + | ENSG00000136244         IL6      protein_coding
  -------
  seqinfo: 338 sequences from an unspecified genome; no seqlengths
AERNASE.hdac9.genedata <- as_tibble(rowRanges(AERNASE.hdac9))

AERNASE.hdac9.genedata

Analyses

The analyses are focused on three elements:

  1. plaque vulnerability phenotypes
  2. clinical status at inclusion (symptoms)
  3. secondary clinical outcome during three (3) years of follow-up

Covariates & other variables

  1. Age (continuous in 1-year increment). [Age]
  2. Sex (male vs. female). [Gender]
  3. Presence of hypertension at baseline (defined either as history of hypertension, SBP ≥140 mm Hg, DBP ≥90 mm Hg, or prescription of antihypertensive medications). [Hypertension.composite]
  4. Presence of diabetes mellitus at baseline (defined either as a history of diabetes and/or administration of glucose lowering medication). [DiabetesStatus]
  5. Smoking (current, ex-, never). [SmokerStatus]
  6. LDL-C levels (continuous). [LDL_final]
  7. Use of lipid-lowering drugs. [Med.Statin.LLD]
  8. Use of antiplatelet drugs. [Med.all.antiplatelet]
  9. eGFR (continuous). [GFR_MDRD]
  10. BMI (continuous). [BMI]
  11. History of cardiovascular disease (stroke, coronary artery disease, peripheral artery disease). [MedHx_CVD] combination of [CAD_history, Stroke_history, Peripheral.interv]
  12. Level of stenosis (50-70% vs. 70-99%). [stenose]
  13. Year of surgery [ORdate_year] as we discovered in Van Lammeren et al. the composition of the plaque and therefore the Athero-Express Biobank Study has changed over the years. Likely through changes in lifestyle and primary prevention regimes.

Models

We will analyze the data through four different models

  • Model 1: adjusted for age, sex, and year of surgery
  • Model 2: adjusted for age, sex, year of surgery, and additionally adjusted for history hypertension (defined from medical history and/or use of antihypertensive medications), diabetes (defined as history of a diagnosis and/or use of glucose-lowering medications), current smoking, LDL-C levels at time of operation, use of statins, use of antiplatelet agents, eGFR, BMI, history of cardiovascular disease (coronary artery disease, stroke, peripheral artery disease), and level of stenosis (50-70%, 70-90%, 90-99%)

A. Cross-sectional analysis plaque phenotypes

In the cross-sectional analysis of plaque MCP1 levels we will focus on the following plaque vulnerability phenotypes:

  • Percentage of macrophages (continuous trait)
  • Percentage of SMCs (continuous trait)
  • Number of intraplaque microvessels per 3-4 hotspots (continuous trait)
  • Presence of moderate/heavy calcifications (binary trait)
  • Presence of moderate/heavy collagen content (binary trait)
  • Presence of lipid core no/<10% vs. >10% (binary trait)
  • Presence of intraplaque hemorrhage (binary trait)

Quantitative traits

We inspect the plaque characteristics, and inverse-rank normal transformation continuous phenotypes.


# macrophages
cat("Summary of data.\n")
Summary of data.
summary(AERNASE.clin.hdac9$MAC_rankNorm)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
-2.0937 -0.3666  0.3275  0.2830  0.9611  2.6777      15 
min_macmean <- min(AERNASE.clin.hdac9$MAC_rankNorm, na.rm = TRUE)
cat(paste0("\nMinimum value % macrophages: ",min_macmean,".\n"))

Minimum value % macrophages: -2.09371245439672.
ggpubr::gghistogram(AERNASE.clin.hdac9, "MAC_rankNorm", 
                    # y = "..count..", 
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"), 
                    add = "median", 
                    #add_density = TRUE,
                    rug = TRUE,
                    #add.params =  list(color = "black", linetype = 2), 
                    title = "% macrophages",
                    xlab = "inverse-rank normalized %", 
                    ggtheme = theme_minimal())
Warning: Using `bins = 30` by default. Pick better value with the argument `bins`.
Warning: Removed 15 rows containing non-finite values (stat_bin).

# smooth muscle cells
cat("Summary of data.\n")
Summary of data.
summary(AERNASE.clin.hdac9$SMC_rankNorm)
    Min.  1st Qu.   Median     Mean  3rd Qu.     Max.     NA's 
-2.64372 -0.65587 -0.02105 -0.03204  0.63927  2.55750       17 
min_smcmean <- min(AERNASE.clin.hdac9$SMC_rankNorm, na.rm = TRUE)
cat(paste0("\nMinimum value % smooth muscle cells: ",min_smcmean,".\n"))

Minimum value % smooth muscle cells: -2.64372511353808.
ggpubr::gghistogram(AERNASE.clin.hdac9, "SMC_rankNorm", 
                    # y = "..count..", 
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"), 
                    add = "median", 
                    #add_density = TRUE,
                    rug = TRUE,
                    #add.params =  list(color = "black", linetype = 2), 
                    title = "% smooth muscle cells",
                    xlab = "inverse-rank normalized %", 
                    ggtheme = theme_minimal())
Warning: Using `bins = 30` by default. Pick better value with the argument `bins`.
Warning: Removed 17 rows containing non-finite values (stat_bin).

# vessel density
cat("Summary of data.\n")
Summary of data.
summary(AERNASE.clin.hdac9$VesselDensity_rankNorm)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
-1.9203 -0.4576  0.1109  0.1365  0.7694  3.1703      53 
min_vesseldensity <- min(AERNASE.clin.hdac9$VesselDensity_rankNorm, na.rm = TRUE)
min_vesseldensity
[1] -1.920282
cat(paste0("\nMinimum value number of intraplaque neovessels per 3-4 hotspots: ",min_vesseldensity,".\n"))

Minimum value number of intraplaque neovessels per 3-4 hotspots: -1.92028163343836.
ggpubr::gghistogram(AERNASE.clin.hdac9, "VesselDensity_rankNorm", 
                    # y = "..count..", 
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"), 
                    add = "median", 
                    #add_density = TRUE,
                    rug = TRUE,
                    #add.params =  list(color = "black", linetype = 2), 
                    title = "number of intraplaque neovessels per 3-4 hotspots",
                   xlab = "inverse-rank normalized number", 
                    ggtheme = theme_minimal())
Warning: Using `bins = 30` by default. Pick better value with the argument `bins`.
Warning: Removed 53 rows containing non-finite values (stat_bin).

Given their strong correlation, we also introduce a macrophages/smooth muscle cell ratio. This is a proxy of the extend to which a plaque is inflammed (‘unstable’) as compared to ‘stable’.


AERNASE.clin.hdac9$MAC_SMC_ratio <- AERNASE.clin.hdac9$MAC_rankNorm / AERNASE.clin.hdac9$SMC_rankNorm

AERNASE.clin.hdac9$MAC_SMC_ratio_rank  <- qnorm((rank(AERNASE.clin.hdac9$MAC_SMC_ratio, na.last = "keep") - 0.5) / sum(!is.na(AERNASE.clin.hdac9$MAC_SMC_ratio)))


cat("Summary of data.\n")
Summary of data.
summary(AERNASE.clin.hdac9$MAC_rankNorm)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
-2.0937 -0.3666  0.3275  0.2830  0.9611  2.6777      15 
summary(AERNASE.clin.hdac9$SMC_rankNorm)
    Min.  1st Qu.   Median     Mean  3rd Qu.     Max.     NA's 
-2.64372 -0.65587 -0.02105 -0.03204  0.63927  2.55750       17 
summary(AERNASE.clin.hdac9$MAC_SMC_ratio_rank)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
-3.1469 -0.6732  0.0000  0.0000  0.6732  3.1469      17 
ggpubr::gghistogram(AERNASE.clin.hdac9, "MAC_SMC_ratio_rank", 
                    # y = "..count..", 
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"), 
                    add = "median", 
                    #add_density = TRUE,
                    rug = TRUE,
                    #add.params =  list(color = "black", linetype = 2), 
                    title = "macrophages/smooth muscle cells ratio",
                    xlab = "inverse-rank normalized", 
                    ggtheme = theme_minimal())
Warning: Using `bins = 30` by default. Pick better value with the argument `bins`.
Warning: Removed 17 rows containing non-finite values (stat_bin).

Binary traits

library(dplyr)
# calcification
cat("Summary of data.\n")
Summary of data.
summary(AERNASE.clin.hdac9$Calc.bin)
      no/minor moderate/heavy           NA's 
           285            330              8 
contrasts(AERNASE.clin.hdac9$Calc.bin)
               moderate/heavy
no/minor                    0
moderate/heavy              1
AERNASE.clin.hdac9$CalcificationPlaque <- as.factor(AERNASE.clin.hdac9$Calc.bin)

df <- AERNASE.clin.hdac9 %>%
  dplyr::filter(!is.na(CalcificationPlaque)) %>%
  dplyr::group_by(Gender, CalcificationPlaque) %>%
dplyr::summarise(counts = n()) 
`summarise()` has grouped output by 'Gender'. You can override using the `.groups` argument.
ggpubr::ggbarplot(df, x = "CalcificationPlaque", y = "counts",
                    # y = "..count..",
                    color = "white",
                    fill = "Gender",
                    palette = c("#DB003F", "#1290D9"),
                    label = TRUE, lab.vjust = 2, lab.col = "#FFFFFF",
                    title = "Calcification",
                    xlab = "calcification", 
                    ggtheme = theme_minimal())

rm(df)

# collagen
cat("Summary of data.\n")
Summary of data.
summary(AERNASE.clin.hdac9$Collagen.bin)
      no/minor moderate/heavy           NA's 
           118            496              9 
contrasts(AERNASE.clin.hdac9$Collagen.bin)
               moderate/heavy
no/minor                    0
moderate/heavy              1
AERNASE.clin.hdac9$CollagenPlaque <- as.factor(AERNASE.clin.hdac9$Collagen.bin)

df <- AERNASE.clin.hdac9 %>%
  dplyr::filter(!is.na(CollagenPlaque)) %>%
  dplyr::group_by(Gender, CollagenPlaque) %>%
dplyr::summarise(counts = n()) 
`summarise()` has grouped output by 'Gender'. You can override using the `.groups` argument.
ggpubr::ggbarplot(df, x = "CollagenPlaque", y = "counts",
                    # y = "..count..",
                    color = "white",
                    fill = "Gender",
                    palette = c("#DB003F", "#1290D9"),
                    label = TRUE, lab.vjust = 2, lab.col = "#FFFFFF",
                    title = "Collagen",
                    xlab = "collagen", 
                    ggtheme = theme_minimal())

rm(df)

# fat 10%
cat("Summary of data.\n")
Summary of data.
summary(AERNASE.clin.hdac9$Fat.bin_10)
 <10%  >10%  NA's 
  145   470     8 
contrasts(AERNASE.clin.hdac9$Fat.bin_10)
       >10%
 <10%     0
 >10%     1
AERNASE.clin.hdac9$Fat10Perc <- as.factor(AERNASE.clin.hdac9$Fat.bin_10)

df <- AERNASE.clin.hdac9 %>%
  dplyr::filter(!is.na(Fat10Perc)) %>%
  dplyr::group_by(Gender, Fat10Perc) %>%
dplyr::summarise(counts = n()) 
`summarise()` has grouped output by 'Gender'. You can override using the `.groups` argument.
ggpubr::ggbarplot(df, x = "Fat10Perc", y = "counts",
                    # y = "..count..",
                    color = "white",
                    fill = "Gender",
                    palette = c("#DB003F", "#1290D9"),
                    label = TRUE, lab.vjust = 2, lab.col = "#FFFFFF",
                    title = "Intraplaque fat",
                    xlab = "intraplaque fat", 
                    ggtheme = theme_minimal())

rm(df)

# macrophages binned
cat("Summary of data.\n")
Summary of data.
summary(AERNASE.clin.hdac9$Macrophages.bin)
      no/minor moderate/heavy           NA's 
           265            347             11 
contrasts(AERNASE.clin.hdac9$Macrophages.bin)
               moderate/heavy
no/minor                    0
moderate/heavy              1
AERNASE.clin.hdac9$MAC_binned <- as.factor(AERNASE.clin.hdac9$Macrophages.bin)

df <- AERNASE.clin.hdac9 %>%
  dplyr::filter(!is.na(MAC_binned)) %>%
  dplyr::group_by(Gender, MAC_binned) %>%
dplyr::summarise(counts = n()) 
`summarise()` has grouped output by 'Gender'. You can override using the `.groups` argument.
ggpubr::ggbarplot(df, x = "MAC_binned", y = "counts",
                    # y = "..count..",
                    color = "white",
                    fill = "Gender",
                    palette = c("#DB003F", "#1290D9"),
                    label = TRUE, lab.vjust = 2, lab.col = "#FFFFFF",
                    title = "Macrophages (binned)",
                    xlab = "Macrophages", 
                    ggtheme = theme_minimal())

rm(df)



# SMC binned
cat("Summary of data.\n")
Summary of data.
summary(AERNASE.clin.hdac9$SMC.bin)
      no/minor moderate/heavy           NA's 
           194            418             11 
contrasts(AERNASE.clin.hdac9$SMC.bin)
               moderate/heavy
no/minor                    0
moderate/heavy              1
AERNASE.clin.hdac9$SMC_binned <- as.factor(AERNASE.clin.hdac9$SMC.bin)

df <- AERNASE.clin.hdac9 %>%
  dplyr::filter(!is.na(SMC_binned)) %>%
  dplyr::group_by(Gender, SMC_binned) %>%
dplyr::summarise(counts = n()) 
`summarise()` has grouped output by 'Gender'. You can override using the `.groups` argument.
ggpubr::ggbarplot(df, x = "SMC_binned", y = "counts",
                    # y = "..count..",
                    color = "white",
                    fill = "Gender",
                    palette = c("#DB003F", "#1290D9"),
                    label = TRUE, lab.vjust = 2, lab.col = "#FFFFFF",
                    title = "SMC (binned)",
                    xlab = "SMC", 
                    ggtheme = theme_minimal())

rm(df)




# IPH
cat("Summary of data.\n")
Summary of data.
summary(AERNASE.clin.hdac9$IPH.bin)
  no  yes NA's 
 235  379    9 
contrasts(AERNASE.clin.hdac9$IPH.bin)
    yes
no    0
yes   1
AERNASE.clin.hdac9$IPH <- as.factor(AERNASE.clin.hdac9$IPH.bin)

df <- AERNASE.clin.hdac9 %>%
  dplyr::filter(!is.na(IPH)) %>%
  dplyr::group_by(Gender, IPH) %>%
dplyr::summarise(counts = n()) 
`summarise()` has grouped output by 'Gender'. You can override using the `.groups` argument.
ggpubr::ggbarplot(df, x = "IPH", y = "counts",
                    # y = "..count..",
                    color = "white",
                    fill = "Gender",
                    palette = c("#DB003F", "#1290D9"),
                    label = TRUE, lab.vjust = 2, lab.col = "#FFFFFF",
                    title = "Intraplaque hemorrhage",
                    xlab = "intraplaque hemorrhage", 
                    ggtheme = theme_minimal())

rm(df)

# Symptoms
cat("Summary of data.\n")
Summary of data.
summary(AERNASE.clin.hdac9$AsymptSympt)
     Asymptomatic Ocular and others       Symptomatic 
               80               139               404 
contrasts(AERNASE.clin.hdac9$AsymptSympt)
                  Ocular and others Symptomatic
Asymptomatic                      0           0
Ocular and others                 1           0
Symptomatic                       0           1
AERNASE.clin.hdac9$AsymptSympt <- as.factor(AERNASE.clin.hdac9$AsymptSympt)

df <- AERNASE.clin.hdac9 %>%
  dplyr::filter(!is.na(AsymptSympt)) %>%
  dplyr::group_by(Gender, AsymptSympt) %>%
dplyr::summarise(counts = n()) 
`summarise()` has grouped output by 'Gender'. You can override using the `.groups` argument.
ggpubr::ggbarplot(df, x = "AsymptSympt", y = "counts",
                    # y = "..count..",
                    color = "white",
                    fill = "Gender",
                    palette = c("#DB003F", "#1290D9"),
                    label = TRUE, lab.vjust = 2, lab.col = "#FFFFFF",
                    title = "Symptoms",
                    xlab = "symptoms", 
                    ggtheme = theme_minimal())

rm(df)

Correlations between HDAC9 plaque levels and surgery year

Here we compare the HDAC9 plaque gene expression levels.

p1 <- ggpubr::ggscatter(AERNASE.clin.hdac9, 
                        x = "ORyear", 
                        y = "HDAC9",
                        color = "#1290D9",
                        # fill = "Gender",
                        # palette = c("#1290D9", "#DB003F"),
                        add = "reg.line",
                        add.params =  list(color = "black", linetype = 2),
                        cor.coef = TRUE, cor.method = "spearman", 
                        xlab = "year of surgery",
                        ylab = "experiment 1",
                        title = "HDAC9 (normalized expression)",
                        ggtheme = theme_minimal())

p1 
`geom_smooth()` using formula 'y ~ x'

rm(p1)

Prepare modeling

In this section we make some variables to assist with analysis.

We fix the diabetes status variable.


# Fix diabetes
attach(AEDB.CEA)
AEDB.CEA[,"DiabetesStatus"] <- NA
AEDB.CEA$DiabetesStatus[DM.composite == -999] <- NA
AEDB.CEA$DiabetesStatus[DM.composite == 0] <- "Control (no Diabetes Dx/Med)"
AEDB.CEA$DiabetesStatus[DM.composite == 1] <- "Diabetes"
detach(AEDB.CEA)

table(AEDB.CEA$DM.composite, AEDB.CEA$DiabetesStatus)
   
    Control (no Diabetes Dx/Med) Diabetes
  0                         1820        0
  1                            0      574
# AEDB.CEA.temp <- subset(AEDB.CEA,  select = c("STUDY_NUMBER", "UPID", "Age", "Gender", "Hospital", "Artery_summary", "DM.composite", "DiabetesStatus"))
# require(labelled)
# AEDB.CEA.temp$Gender <- to_factor(AEDB.CEA.temp$Gender)
# AEDB.CEA.temp$Hospital <- to_factor(AEDB.CEA.temp$Hospital)
# AEDB.CEA.temp$Artery_summary <- to_factor(AEDB.CEA.temp$Artery_summary)
# AEDB.CEA.temp$DiabetesStatus <- to_factor(AEDB.CEA.temp$DiabetesStatus)
# 
# DT::datatable(AEDB.CEA.temp[1:10,], caption = "Excerpt of the whole AEDB.CEA.", rownames = FALSE)
# 
# rm(AEDB.CEA.temp)

We will also fix a history of CAD, stroke or peripheral intervention status variable. This will be based on CAD_history, Stroke_history, and Peripheral.interv

# AEDB.CEA$CAD_history
# AEDB.CEA$Stroke_history
# AEDB.CEA$Peripheral.interv

# Fix diabetes
attach(AEDB.CEA)
AEDB.CEA[,"MedHx_CVD"] <- NA
AEDB.CEA$MedHx_CVD[CAD_history == "No history CAD" | Stroke_history == 0 | Peripheral.interv == "no"] <- "No"
AEDB.CEA$MedHx_CVD[CAD_history == "History CAD" | Stroke_history == 1 | Peripheral.interv == "yes"] <- "yes"
detach(AEDB.CEA)

table(AEDB.CEA$CAD_history)

       Missing No history CAD    History CAD 
             0           1618            756 
table(AEDB.CEA$Stroke_history)

   0    1 
1642  779 
table(AEDB.CEA$Peripheral.interv)

  no  yes 
1868  482 
table(AEDB.CEA$MedHx_CVD)

  No  yes 
 892 1529 
# AEDB.CEA.temp <- subset(AEDB.CEA,  select = c("STUDY_NUMBER", "UPID", "Age", "Gender", "Hospital", "Artery_summary", "diet810", "AlcoholUse"))
# require(labelled)
# AEDB.CEA.temp$Gender <- to_factor(AEDB.CEA.temp$Gender)
# AEDB.CEA.temp$Hospital <- to_factor(AEDB.CEA.temp$Hospital)
# AEDB.CEA.temp$Artery_summary <- to_factor(AEDB.CEA.temp$Artery_summary)
# AEDB.CEA.temp$AlcoholUse <- to_factor(AEDB.CEA.temp$AlcoholUse)
# 
# DT::datatable(AEDB.CEA.temp[1:10,], caption = "Excerpt of the whole AEDB.CEA.", rownames = FALSE)
# 
# rm(AEDB.CEA.temp)
AERNASE.clin.hdac9$DiabetesStatus <- NULL
AERNASE.clin.hdac9$MedHx_CVD <- NULL

AERNASE.clin.hdac9 <- merge(AERNASE.clin.hdac9, 
                            subset(AEDB.CEA, select = c("STUDY_NUMBER", "dateok", "DiabetesStatus", "MedHx_CVD")), 
                            by.x = "STUDY_NUMBER", by.y = "STUDY_NUMBER", sort = TRUE)

AERNASE.clin.hdac9$MedHx_CVD <- as_factor(AERNASE.clin.hdac9$MedHx_CVD)
AERNASE.clin.hdac9$DiabetesStatus <- as_factor(AERNASE.clin.hdac9$DiabetesStatus)

study.samplesize = nrow(AERNASE.clin.hdac9) # study.samplesize is an expected variable in the GLM.BIN() GLM.CON() functions

TRAITS.TARGET.RANK = c("HDAC9")

TRAITS.CON.RANK = c("MAC_rankNorm", "SMC_rankNorm", "MAC_SMC_ratio_rank", "VesselDensity_rankNorm")

TRAITS.BIN = c("CalcificationPlaque", "CollagenPlaque", "Fat10Perc", "IPH",
               "MAC_binned", "SMC_binned")

# "Hospital", 
# "Age", "Gender", 
# "TC_final", "LDL_final", "HDL_final", "TG_final", 
# "systolic", "diastoli", "GFR_MDRD", "BMI", 
# "KDOQI", "BMI_WHO",
# "SmokerCurrent", "eCigarettes", "ePackYearsSmoking",
# "DiabetesStatus", "Hypertension.composite", 
# "Hypertension.drugs", "Med.anticoagulants", "Med.all.antiplatelet", "Med.Statin.LLD", 
# "Stroke_Dx", "sympt", "Symptoms.5G", "restenos",
# "EP_composite", "EP_composite_time",
# "macmean0", "smcmean0", "Macrophages.bin", "SMC.bin",
# "neutrophils", "Mast_cells_plaque",
# "IPH.bin", "vessel_density_averaged",
# "Calc.bin", "Collagen.bin", 
# "Fat.bin_10", "Fat.bin_40", "OverallPlaquePhenotype",
# "IL6_pg_ug_2015", "MCP1_pg_ug_2015", 
# "QC2018_FILTER", "CHIP", "SAMPLE_TYPE",
# "CAD_history", "Stroke_history", "Peripheral.interv",
# "stenose"

# 1.  Age (continuous in 1-year increment). [Age]
# 2.  Sex (male vs. female). [Gender]
# 3.  Presence of hypertension at baseline (defined either as history of hypertension, SBP ≥140 mm Hg, DBP ≥90 mm Hg, or prescription of antihypertensive medications). [Hypertension.composite]
# 4.  Presence of diabetes mellitus at baseline (defined either as a history of diabetes, administration of glucose lowering medication, HbA1c ≥6.5%, fasting glucose ≥126 mg/dl, .or random glucose levels ≥200 mg/dl). [DiabetesStatus]
# 5.  Smoking (current, ex-, never). [SmokerCurrent]
# 6.  LDL-C levels (continuous). [LDL_final]
# 7.  Use of lipid-lowering drugs. [Med.Statin.LLD]
# 8.  Use of antiplatelet drugs. [Med.all.antiplatelet]
# 9.  eGFR (continuous). [GFR_MDRD]
# 10.   BMI (continuous). [BMI]
# 11.   History of cardiovascular disease (stroke, coronary artery disease, peripheral artery disease). [MedHx_CVD] combinatino of: [CAD_history, Stroke_history, Peripheral.interv]
# 12.   Level of stenosis (50-70% vs. 70-99%). [stenose]

# Models 
# Model 1: adjusted for age and sex
# Model 2: adjusted for age, sex, hypertension, diabetes, smoking, LDL-C levels, lipid-lowering drugs, antiplatelet drugs, eGFR, BMI, history of CVD, level of stenosis,

AERNASE.clin.hdac9$ORdate_epoch <- as.numeric(AERNASE.clin.hdac9$dateok)
AERNASE.clin.hdac9$ORdate_year <- AERNASE.clin.hdac9$ORyear

cat("Summary of 'year of surgery' in 'epoch' (); coded as `numeric()`\n")
Summary of 'year of surgery' in 'epoch' (); coded as `numeric()`
summary(AERNASE.clin.hdac9$ORdate_epoch)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  11770   12759   13440   13553   14287   16807 
cat("\nSummary of 'year of surgery' in 'years' (); coded as `factor()`\n")

Summary of 'year of surgery' in 'years' (); coded as `factor()`
table(AERNASE.clin.hdac9$ORdate_year)

No data available/missing                      2002                      2003                      2004                      2005                      2006 
                        0                        32                        62                        66                        82                        85 
                     2007                      2008                      2009                      2010                      2011                      2012 
                       67                        63                        67                        34                        31                        22 
                     2013                      2014                      2015                      2016                      2017                      2018 
                        5                         3                         3                         1                         0                         0 
                     2019 
                        0 
# COVARIATES_M1 = c("Age", "Gender", "ORdate_year")
COVARIATES_M1 = c("Age", "Gender", "ORdate_epoch")

COVARIATES_M2 = c(COVARIATES_M1,  
               "Hypertension.composite", "DiabetesStatus", 
               "SmokerStatus", 
               # "SmokerCurrent",
               "Med.Statin.LLD", "Med.all.antiplatelet", 
               "GFR_MDRD", "BMI", 
               # "CAD_history", "Stroke_history", "Peripheral.interv", 
               "MedHx_CVD",
               "stenose")
str(AERNASE.clin.hdac9)
'data.frame':   623 obs. of  74 variables:
 $ STUDY_NUMBER               : chr  "ae1" "ae1026" "ae1029" "ae1032" ...
 $ SampleType                 : chr  "plaque" "plaque" "plaque" "plaque" ...
 $ RNAseqType                 : chr  "3' RNAseq" "3' RNAseq" "3' RNAseq" "3' RNAseq" ...
 $ Hospital                   : Factor w/ 2 levels "St. Antonius, Nieuwegein",..: 1 2 2 2 2 2 2 1 2 2 ...
 $ ORyear                     : Factor w/ 19 levels "No data available/missing",..: 2 5 5 5 5 5 6 3 5 7 ...
 $ Artery_summary             : Factor w/ 9 levels "No artery known (yet), no surgery (patient ill, died, exited study), re-numbered to AAA",..: 2 2 2 2 2 2 2 2 2 2 ...
 $ Age                        : num  63 80 57 49 75 71 77 78 60 59 ...
 $ Gender                     : Factor w/ 2 levels "female","male": 2 2 2 1 1 1 2 2 2 1 ...
 $ TC_final                   : num  4.48 NA 4.2 NA 2.92 ...
 $ LDL_final                  : num  2.94 NA NA NA 0.65 ...
 $ HDL_final                  : num  1 NA NA NA 0.87 ...
 $ TG_final                   : num  1.21 NA NA NA 3.07 ...
 $ systolic                   : num  220 160 150 170 104 180 160 165 140 150 ...
 $ diastoli                   : num  90 80 83 90 55 90 90 70 60 52 ...
 $ GFR_MDRD                   : num  50.9 63.3 76.3 143.9 34.9 ...
 $ BMI                        : num  28.6 26.5 21.6 22 30.8 ...
 $ KDOQI                      : Factor w/ 6 levels "No data available/missing",..: 4 3 3 2 4 5 3 4 4 4 ...
 $ BMI_WHO                    : Factor w/ 5 levels "No data available/missing",..: 4 4 3 3 5 4 4 3 4 3 ...
 $ SmokerStatus               : Factor w/ 3 levels "Current smoker",..: 2 NA 1 1 3 1 3 3 3 1 ...
 $ AlcoholUse                 : Factor w/ 2 levels "No","Yes": 1 2 1 2 2 1 2 1 2 1 ...
 $ Hypertension.selfreport    : Factor w/ 3 levels "No data available/missing",..: 3 2 2 2 3 3 3 3 3 3 ...
 $ Hypertension.selfreportdrug: Factor w/ 3 levels "No data available/missing",..: 3 2 2 2 3 2 3 3 3 3 ...
 $ Hypertension.composite     : Factor w/ 3 levels "No data available/missing",..: 3 3 2 2 3 3 3 3 3 3 ...
 $ Hypertension.drugs         : Factor w/ 3 levels "No data available/missing",..: 2 3 2 2 3 3 3 3 3 3 ...
 $ Med.anticoagulants         : Factor w/ 3 levels "No data available/missing",..: 2 3 2 2 2 3 2 3 2 2 ...
 $ Med.all.antiplatelet       : Factor w/ 3 levels "No data available/missing",..: 3 2 3 3 3 2 3 3 3 3 ...
 $ Med.Statin.LLD             : Factor w/ 3 levels "No data available/missing",..: 2 3 3 2 3 3 3 2 3 3 ...
 $ Stroke_Dx                  : Factor w/ 3 levels "Missing","No stroke diagnosed",..: 2 2 2 2 2 2 2 2 2 2 ...
 $ sympt                      : Factor w/ 18 levels "missing","Asymptomatic",..: 3 3 5 3 6 4 2 2 2 2 ...
 $ Symptoms.5G                : Factor w/ 6 levels "Asymptomatic",..: 6 6 5 6 2 5 1 1 1 1 ...
 $ AsymptSympt                : Factor w/ 3 levels "Asymptomatic",..: 3 3 3 3 2 3 1 1 1 1 ...
 $ AsymptSympt2G              : Factor w/ 2 levels "Asymptomatic",..: 2 2 2 2 2 2 1 1 1 1 ...
 $ Symptoms.Update2G          : Factor w/ 2 levels "Asymptomatic",..: 2 2 2 2 2 2 1 1 1 1 ...
 $ Symptoms.Update3G          : Factor w/ 3 levels "Asymptomatic",..: 2 2 2 2 2 2 1 1 1 1 ...
 $ restenos                   : Factor w/ 4 levels "missing","de novo",..: 2 2 2 2 2 2 2 2 2 2 ...
 $ stenose                    : Factor w/ 10 levels "missing","0-49%",..: 5 3 4 4 5 4 4 5 4 4 ...
 $ CAD_history                : Factor w/ 3 levels "Missing","No history CAD",..: 3 2 2 2 2 2 2 2 2 2 ...
 $ PAOD                       : Factor w/ 3 levels "missing/no data",..: 3 3 2 2 2 2 2 2 2 3 ...
 $ Peripheral.interv          : Factor w/ 2 levels "no","yes": 1 1 1 1 1 1 1 1 1 2 ...
 $ EP_composite               : Factor w/ 3 levels "No data available.",..: 2 3 3 2 3 3 3 2 2 2 ...
 $ EP_composite_time          : num  12.258 0.208 0.685 1.288 2.603 ...
 $ EP_major                   : Factor w/ 3 levels "No data available.",..: 2 2 2 2 3 3 3 2 2 2 ...
 $ EP_major_time              : num  12.258 0.647 2.989 1.288 2.603 ...
 $ MAC_rankNorm               : num  -0.0695 -0.7504 0.9099 -0.3812 -1.0959 ...
 $ SMC_rankNorm               : num  1.526 0.58 0.674 1.26 0.341 ...
 $ Macrophages.bin            : Factor w/ 2 levels "no/minor","moderate/heavy": 2 1 2 1 1 2 2 1 2 1 ...
 $ SMC.bin                    : Factor w/ 2 levels "no/minor","moderate/heavy": 2 2 2 2 2 2 1 2 2 2 ...
 $ Neutrophils_rankNorm       : num  NA 0.591 0.118 -1.618 -1.618 ...
 $ MastCells_rankNorm         : num  NA 0.633 NA NA NA ...
 $ IPH.bin                    : Factor w/ 2 levels "no","yes": 1 2 2 1 1 1 1 2 1 1 ...
 $ VesselDensity_rankNorm     : num  -0.723 0.769 0.854 1.14 -0.861 ...
 $ Calc.bin                   : Factor w/ 2 levels "no/minor","moderate/heavy": 2 2 2 1 1 1 2 2 2 1 ...
 $ Collagen.bin               : Factor w/ 2 levels "no/minor","moderate/heavy": 2 2 2 2 2 2 1 2 2 2 ...
 $ Fat.bin_10                 : Factor w/ 2 levels " <10%"," >10%": 2 2 2 2 1 1 2 1 2 1 ...
 $ Fat.bin_40                 : Factor w/ 2 levels "<40%",">40%": 1 1 1 1 1 1 2 1 1 1 ...
 $ OverallPlaquePhenotype     : Factor w/ 3 levels "atheromatous",..: 2 3 2 2 3 3 1 3 2 3 ...
 $ Plaque_Vulnerability_Index : Factor w/ 6 levels "0","1","2","3",..: 3 3 4 2 1 2 5 2 3 1 ...
 $ HDAC9                      : int  24 11 7 5 13 46 14 0 30 12 ...
 $ TWIST1                     : int  1 13 5 0 1 1 3 1 19 2 ...
 $ IL1B                       : int  2 4 2 1 1 2 5 2 43 2 ...
 $ IL6                        : int  0 1 0 0 0 0 0 0 5 0 ...
 $ MAC_SMC_ratio              : num  -0.0455 -1.2938 1.3509 -0.3026 -3.2115 ...
 $ MAC_SMC_ratio_rank         : num  -0.215 -0.888 0.722 -0.366 -1.331 ...
 $ CalcificationPlaque        : Factor w/ 2 levels "no/minor","moderate/heavy": 2 2 2 1 1 1 2 2 2 1 ...
 $ CollagenPlaque             : Factor w/ 2 levels "no/minor","moderate/heavy": 2 2 2 2 2 2 1 2 2 2 ...
 $ Fat10Perc                  : Factor w/ 2 levels " <10%"," >10%": 2 2 2 2 1 1 2 1 2 1 ...
 $ MAC_binned                 : Factor w/ 2 levels "no/minor","moderate/heavy": 2 1 2 1 1 2 2 1 2 1 ...
 $ SMC_binned                 : Factor w/ 2 levels "no/minor","moderate/heavy": 2 2 2 2 2 2 1 2 2 2 ...
 $ IPH                        : Factor w/ 2 levels "no","yes": 1 2 2 1 1 1 1 2 1 1 ...
 $ dateok                     : Date, format: "2002-03-26" "2005-09-08" "2005-09-15" "2005-09-29" ...
 $ DiabetesStatus             : Factor w/ 2 levels "Control (no Diabetes Dx/Med)",..: 1 1 1 1 2 1 1 1 1 1 ...
 $ MedHx_CVD                  : Factor w/ 2 levels "yes","No": 1 2 1 2 2 1 2 2 2 1 ...
 $ ORdate_epoch               : num  11772 13034 13041 13055 13083 ...
 $ ORdate_year                : Factor w/ 19 levels "No data available/missing",..: 2 5 5 5 5 5 6 3 5 7 ...
# COVARIATES_M3 = c(COVARIATES_M2, "LDL_final")

# COVARIATES_M4 = c(COVARIATES_M2, "hsCRP_plasma")

Model 1

In this model we correct for Age, Gender, and year of surgery.

Here we use the inverse-rank normalized data - visually this is more normally distributed.

Quantitative plaque traits

detach("package:EnsDb.Hsapiens.v86", unload = TRUE)
detach("package:ensembldb", unload = TRUE)

Analysis of continuous/quantitative plaque traits as a function of plaque HDAC9 expression levels.

library(dplyr)
library(MASS)

GLM.results <- data.frame(matrix(NA, ncol = 15, nrow = 0))

cat("Running linear regression...\n")
Running linear regression...
for (target_of_interest in 1:length(TRAITS.TARGET.RANK)) {
  TARGET = TRAITS.TARGET.RANK[target_of_interest]
  cat(paste0("\nAnalysis of ",TARGET,".\n"))
  for (trait in 1:length(TRAITS.CON.RANK)) {
    TRAIT = TRAITS.CON.RANK[trait]
    cat(paste0("\n- processing ",TRAIT,"\n\n"))
    currentDF <- as.data.frame(AERNASE.clin.hdac9 %>%
      dplyr::select(., TARGET, TRAIT, COVARIATES_M1) %>%
      filter(complete.cases(.))) %>%
      filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
    # # for debug
    # print(DT::datatable(currentDF))
    # print(nrow(currentDF))
    # print(str(currentDF))
    ### univariate
    # fit <- lm(currentDF[,TARGET] ~ currentDF[,TRAIT] + Age + Gender + ORdate_year, data = currentDF)
    fit <- lm(currentDF[,TARGET] ~ currentDF[,TRAIT] + Age + Gender + ORdate_epoch, data = currentDF)
    model_step <- stepAIC(fit, direction = "both", trace = FALSE)
    print(model_step)
    print(summary(fit))

    GLM.results.TEMP <- data.frame(matrix(NA, ncol = 15, nrow = 0))
    GLM.results.TEMP[1,] = GLM.CON(fit, "AERNASE.clin.hdac9", TARGET, TRAIT, verbose = TRUE)
    GLM.results = rbind(GLM.results, GLM.results.TEMP)
  }
}

Analysis of HDAC9.

- processing MAC_rankNorm


Call:
lm(formula = currentDF[, TARGET] ~ 1, data = currentDF)

Coefficients:
(Intercept)  
      28.34  


Call:
lm(formula = currentDF[, TARGET] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch, data = currentDF)

Residuals:
   Min     1Q Median     3Q    Max 
-29.12 -17.76  -7.33   8.41 420.85 

Coefficients:
                    Estimate Std. Error t value Pr(>|t|)
(Intercept)         7.448488  20.604160   0.362    0.718
currentDF[, TRAIT]  0.406087   1.390528   0.292    0.770
Age                 0.104075   0.148668   0.700    0.484
Gendermale         -1.850604   3.049440  -0.607    0.544
ORdate_epoch        0.001114   0.001383   0.805    0.421

Residual standard error: 32.38 on 603 degrees of freedom
Multiple R-squared:  0.002608,  Adjusted R-squared:  -0.004008 
F-statistic: 0.3942 on 4 and 603 DF,  p-value: 0.8129

Analyzing in dataset ' AERNASE.clin.hdac9 ' the association of ' HDAC9 ' with ' MAC_rankNorm ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.hdac9 
Score/Exposure/biomarker..: HDAC9 
Trait/outcome.............: MAC_rankNorm 
Effect size...............: 0.406087 
Standard error............: 1.390528 
Odds ratio (effect size)..: 1.501 
Lower 95% CI..............: 0.098 
Upper 95% CI..............: 22.909 
T-value...................: 0.292038 
P-value...................: 0.7703582 
R^2.......................: 0.002608 
Adjusted r^2..............: -0.004008 
Sample size of AE DB......: 623 
Sample size of model......: 608 
Missing data %............: 2.407705 

- processing SMC_rankNorm


Call:
lm(formula = currentDF[, TARGET] ~ currentDF[, TRAIT], data = currentDF)

Coefficients:
       (Intercept)  currentDF[, TRAIT]  
             28.15               -4.19  


Call:
lm(formula = currentDF[, TARGET] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch, data = currentDF)

Residuals:
   Min     1Q Median     3Q    Max 
-33.64 -17.22  -7.40   7.66 420.33 

Coefficients:
                     Estimate Std. Error t value Pr(>|t|)   
(Intercept)        21.3246475 20.8289123   1.024  0.30634   
currentDF[, TRAIT] -4.1954910  1.4629836  -2.868  0.00428 **
Age                 0.0282566  0.1497052   0.189  0.85035   
Gendermale         -2.8511622  3.0506642  -0.935  0.35037   
ORdate_epoch        0.0005211  0.0013854   0.376  0.70696   
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 32.19 on 601 degrees of freedom
Multiple R-squared:  0.01613,   Adjusted R-squared:  0.009586 
F-statistic: 2.464 on 4 and 601 DF,  p-value: 0.04408

Analyzing in dataset ' AERNASE.clin.hdac9 ' the association of ' HDAC9 ' with ' SMC_rankNorm ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.hdac9 
Score/Exposure/biomarker..: HDAC9 
Trait/outcome.............: SMC_rankNorm 
Effect size...............: -4.195491 
Standard error............: 1.462984 
Odds ratio (effect size)..: 0.015 
Lower 95% CI..............: 0.001 
Upper 95% CI..............: 0.265 
T-value...................: -2.867764 
P-value...................: 0.004278845 
R^2.......................: 0.016134 
Adjusted r^2..............: 0.009586 
Sample size of AE DB......: 623 
Sample size of model......: 606 
Missing data %............: 2.728732 

- processing MAC_SMC_ratio_rank


Call:
lm(formula = currentDF[, TARGET] ~ currentDF[, TRAIT], data = currentDF)

Coefficients:
       (Intercept)  currentDF[, TRAIT]  
            28.284              -2.673  


Call:
lm(formula = currentDF[, TARGET] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch, data = currentDF)

Residuals:
   Min     1Q Median     3Q    Max 
-33.52 -17.33  -7.38   7.23 418.71 

Coefficients:
                    Estimate Std. Error t value Pr(>|t|)  
(Intercept)         5.347972  20.333310   0.263   0.7926  
currentDF[, TRAIT] -2.823637   1.320233  -2.139   0.0329 *
Age                 0.092945   0.148074   0.628   0.5304  
Gendermale         -2.417155   3.051326  -0.792   0.4286  
ORdate_epoch        0.001362   0.001376   0.990   0.3226  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 32.29 on 601 degrees of freedom
Multiple R-squared:  0.0102,    Adjusted R-squared:  0.003617 
F-statistic: 1.549 on 4 and 601 DF,  p-value: 0.1865

Analyzing in dataset ' AERNASE.clin.hdac9 ' the association of ' HDAC9 ' with ' MAC_SMC_ratio_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.hdac9 
Score/Exposure/biomarker..: HDAC9 
Trait/outcome.............: MAC_SMC_ratio_rank 
Effect size...............: -2.823637 
Standard error............: 1.320233 
Odds ratio (effect size)..: 0.059 
Lower 95% CI..............: 0.004 
Upper 95% CI..............: 0.79 
T-value...................: -2.138741 
P-value...................: 0.03285904 
R^2.......................: 0.010204 
Adjusted r^2..............: 0.003617 
Sample size of AE DB......: 623 
Sample size of model......: 606 
Missing data %............: 2.728732 

- processing VesselDensity_rankNorm


Call:
lm(formula = currentDF[, TARGET] ~ 1, data = currentDF)

Coefficients:
(Intercept)  
       28.7  


Call:
lm(formula = currentDF[, TARGET] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch, data = currentDF)

Residuals:
   Min     1Q Median     3Q    Max 
-29.91 -18.26  -7.72   8.32 420.46 

Coefficients:
                    Estimate Std. Error t value Pr(>|t|)
(Intercept)         5.289183  21.803793   0.243    0.808
currentDF[, TRAIT] -0.589271   1.475546  -0.399    0.690
Age                 0.104402   0.156655   0.666    0.505
Gendermale         -2.038897   3.207386  -0.636    0.525
ORdate_epoch        0.001328   0.001464   0.908    0.365

Residual standard error: 33.05 on 565 degrees of freedom
Multiple R-squared:  0.003443,  Adjusted R-squared:  -0.003612 
F-statistic: 0.488 on 4 and 565 DF,  p-value: 0.7446

Analyzing in dataset ' AERNASE.clin.hdac9 ' the association of ' HDAC9 ' with ' VesselDensity_rankNorm ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.hdac9 
Score/Exposure/biomarker..: HDAC9 
Trait/outcome.............: VesselDensity_rankNorm 
Effect size...............: -0.589271 
Standard error............: 1.475546 
Odds ratio (effect size)..: 0.555 
Lower 95% CI..............: 0.031 
Upper 95% CI..............: 10.002 
T-value...................: -0.399358 
P-value...................: 0.6897803 
R^2.......................: 0.003443 
Adjusted r^2..............: -0.003612 
Sample size of AE DB......: 623 
Sample size of model......: 570 
Missing data %............: 8.507223 
cat("Edit the column names...\n")
Edit the column names...
colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
                          "Beta", "s.e.m.",
                          "OR", "low95CI", "up95CI",
                          "T-value", "P-value", "r^2", "r^2_adj", "N", "Model_N", "Perc_Miss")

cat("Correct the variable types...\n")
Correct the variable types...
GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`T-value` <- as.numeric(GLM.results$`T-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2` <- as.numeric(GLM.results$`r^2`)
GLM.results$`r^2_adj` <- as.numeric(GLM.results$`r^2_adj`)
GLM.results$`N` <- as.numeric(GLM.results$`N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)

# Save the data
cat("Writing results to Excel-file...\n")
Writing results to Excel-file...
### Univariate
library(openxlsx)
write.xlsx(GLM.results,
           file = paste0(OUT_loc, "/",Today,".AERNASE.clin.hdac9.Con.Uni.",TRAIT_OF_INTEREST,".PlaquePhenotypes.RANK.MODEL1.xlsx"),
           rowNames = FALSE, colNames = TRUE, sheetName = "Con.Uni.PlaquePheno")

# Removing intermediates
cat("Removing intermediate files...\n")
Removing intermediate files...
rm(TRAIT, trait, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)

Binary plaque traits

Analysis of binary plaque traits as a function of plaque HDAC9 expression levels.


GLM.results <- data.frame(matrix(NA, ncol = 16, nrow = 0))
for (target_of_interest in 1:length(TRAITS.TARGET.RANK)) {
  TARGET = TRAITS.TARGET.RANK[target_of_interest]
  cat(paste0("\nAnalysis of ",TARGET,".\n"))
  for (trait in 1:length(TRAITS.BIN)) {
    TRAIT = TRAITS.BIN[trait]
    cat(paste0("\n- processing ",TRAIT,"\n\n"))
    currentDF <- as.data.frame(AERNASE.clin.hdac9 %>%
      dplyr::select(., TARGET, TRAIT, COVARIATES_M1) %>%
      filter(complete.cases(.))) %>%
      filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
    # for debug
    # print(DT::datatable(currentDF))
    # print(nrow(currentDF))
    # print(str(currentDF))
    # print(class(currentDF[,TRAIT]))
    ### univariate
    # fit <- glm(as.factor(currentDF[,TRAIT]) ~ currentDF[,TARGET] + Age + Gender + ORdate_year,
    #           data  =  currentDF, family = binomial(link = "logit"))
    fit <- glm(as.factor(currentDF[,TRAIT]) ~ currentDF[,TARGET] + Age + Gender + ORdate_epoch,
              data  =  currentDF, family = binomial(link = "logit"))
    
    model_step <- stepAIC(fit, direction = "both", trace = FALSE)
    print(model_step)
    print(summary(fit))
    
    GLM.results.TEMP <- data.frame(matrix(NA, ncol = 16, nrow = 0))
    GLM.results.TEMP[1,] = GLM.BIN(fit, "AERNASE.clin.hdac9", TARGET, TRAIT, verbose = TRUE)
    GLM.results = rbind(GLM.results, GLM.results.TEMP)
  }
}

Analysis of HDAC9.

- processing CalcificationPlaque


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, TARGET] + 
    Gender + ORdate_epoch, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
        (Intercept)  currentDF[, TARGET]           Gendermale         ORdate_epoch  
          8.2993047            0.0058323           -0.3976708           -0.0005923  

Degrees of Freedom: 614 Total (i.e. Null);  611 Residual
Null Deviance:      849.3 
Residual Deviance: 793.8    AIC: 801.8

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, TARGET] + 
    Age + Gender + ORdate_epoch, family = binomial(link = "logit"), 
    data = currentDF)

Deviance Residuals: 
   Min      1Q  Median      3Q     Max  
-1.888  -1.118   0.774   1.044   1.708  

Coefficients:
                      Estimate Std. Error z value Pr(>|z|)    
(Intercept)          7.973e+00  1.342e+00   5.939 2.87e-09 ***
currentDF[, TARGET]  5.798e-03  3.150e-03   1.840   0.0657 .  
Age                  5.704e-03  9.538e-03   0.598   0.5498    
Gendermale          -3.977e-01  1.993e-01  -1.995   0.0460 *  
ORdate_epoch        -5.970e-04  9.076e-05  -6.577 4.79e-11 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 849.28  on 614  degrees of freedom
Residual deviance: 793.43  on 610  degrees of freedom
AIC: 803.43

Number of Fisher Scoring iterations: 4

Analyzing in dataset ' AERNASE.clin.hdac9 ' the association of ' HDAC9 ' with ' CalcificationPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.hdac9 
Score/Exposure/biomarker..: HDAC9 
Trait/outcome.............: CalcificationPlaque 
Effect size...............: 0.005798 
Standard error............: 0.00315 
Odds ratio (effect size)..: 1.006 
Lower 95% CI..............: 1 
Upper 95% CI..............: 1.012 
Z-value...................: 1.840355 
P-value...................: 0.06571617 
Hosmer and Lemeshow r^2...: 0.065755 
Cox and Snell r^2.........: 0.086802 
Nagelkerke's pseudo r^2...: 0.115944 
Sample size of AE DB......: 623 
Sample size of model......: 615 
Missing data %............: 1.284109 

- processing CollagenPlaque


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ 1, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
(Intercept)  
      1.436  

Degrees of Freedom: 613 Total (i.e. Null);  613 Residual
Null Deviance:      600.9 
Residual Deviance: 600.9    AIC: 602.9

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, TARGET] + 
    Age + Gender + ORdate_epoch, family = binomial(link = "logit"), 
    data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-1.9565   0.5931   0.6317   0.6687   1.0963  

Coefficients:
                      Estimate Std. Error z value Pr(>|z|)  
(Intercept)          2.6386980  1.5759345   1.674   0.0941 .
currentDF[, TARGET] -0.0031744  0.0028088  -1.130   0.2584  
Age                  0.0030889  0.0115622   0.267   0.7893  
Gendermale           0.1412298  0.2350181   0.601   0.5479  
ORdate_epoch        -0.0001054  0.0001045  -1.009   0.3131  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 600.95  on 613  degrees of freedom
Residual deviance: 598.35  on 609  degrees of freedom
AIC: 608.35

Number of Fisher Scoring iterations: 4

Analyzing in dataset ' AERNASE.clin.hdac9 ' the association of ' HDAC9 ' with ' CollagenPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.hdac9 
Score/Exposure/biomarker..: HDAC9 
Trait/outcome.............: CollagenPlaque 
Effect size...............: -0.003174 
Standard error............: 0.002809 
Odds ratio (effect size)..: 0.997 
Lower 95% CI..............: 0.991 
Upper 95% CI..............: 1.002 
Z-value...................: -1.130151 
P-value...................: 0.2584124 
Hosmer and Lemeshow r^2...: 0.004323 
Cox and Snell r^2.........: 0.004222 
Nagelkerke's pseudo r^2...: 0.006764 
Sample size of AE DB......: 623 
Sample size of model......: 614 
Missing data %............: 1.444623 

- processing Fat10Perc


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, TARGET] + 
    Gender + ORdate_epoch, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
        (Intercept)  currentDF[, TARGET]           Gendermale         ORdate_epoch  
          7.9906422            0.0194570            0.7733690           -0.0005761  

Degrees of Freedom: 614 Total (i.e. Null);  611 Residual
Null Deviance:      671.8 
Residual Deviance: 617.5    AIC: 625.5

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, TARGET] + 
    Age + Gender + ORdate_epoch, family = binomial(link = "logit"), 
    data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.5274   0.2789   0.5799   0.7497   1.3251  

Coefficients:
                      Estimate Std. Error z value Pr(>|z|)    
(Intercept)          7.2554211  1.5353858   4.725 2.30e-06 ***
currentDF[, TARGET]  0.0193811  0.0054251   3.573 0.000354 ***
Age                  0.0127860  0.0110204   1.160 0.245962    
Gendermale           0.7773361  0.2217286   3.506 0.000455 ***
ORdate_epoch        -0.0005865  0.0001039  -5.646 1.65e-08 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 671.77  on 614  degrees of freedom
Residual deviance: 616.15  on 610  degrees of freedom
AIC: 626.15

Number of Fisher Scoring iterations: 5

Analyzing in dataset ' AERNASE.clin.hdac9 ' the association of ' HDAC9 ' with ' Fat10Perc ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.hdac9 
Score/Exposure/biomarker..: HDAC9 
Trait/outcome.............: Fat10Perc 
Effect size...............: 0.019381 
Standard error............: 0.005425 
Odds ratio (effect size)..: 1.02 
Lower 95% CI..............: 1.009 
Upper 95% CI..............: 1.03 
Z-value...................: 3.572511 
P-value...................: 0.0003535745 
Hosmer and Lemeshow r^2...: 0.082805 
Cox and Snell r^2.........: 0.08648 
Nagelkerke's pseudo r^2...: 0.13013 
Sample size of AE DB......: 623 
Sample size of model......: 615 
Missing data %............: 1.284109 

- processing IPH


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ Gender + ORdate_epoch, 
    family = binomial(link = "logit"), data = currentDF)

Coefficients:
 (Intercept)    Gendermale  ORdate_epoch  
   7.8612128     0.5675364    -0.0005752  

Degrees of Freedom: 613 Total (i.e. Null);  611 Residual
Null Deviance:      817.1 
Residual Deviance: 768.2    AIC: 774.2

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, TARGET] + 
    Age + Gender + ORdate_epoch, family = binomial(link = "logit"), 
    data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-1.9080  -1.1993   0.7252   0.9502   1.6768  

Coefficients:
                      Estimate Std. Error z value Pr(>|z|)    
(Intercept)          7.555e+00  1.351e+00   5.591 2.26e-08 ***
currentDF[, TARGET]  1.453e-03  2.864e-03   0.507  0.61187    
Age                  4.985e-03  9.737e-03   0.512  0.60868    
Gendermale           5.710e-01  1.982e-01   2.881  0.00396 ** 
ORdate_epoch        -5.810e-04  9.146e-05  -6.353 2.12e-10 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 817.10  on 613  degrees of freedom
Residual deviance: 767.62  on 609  degrees of freedom
AIC: 777.62

Number of Fisher Scoring iterations: 4

Analyzing in dataset ' AERNASE.clin.hdac9 ' the association of ' HDAC9 ' with ' IPH ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.hdac9 
Score/Exposure/biomarker..: HDAC9 
Trait/outcome.............: IPH 
Effect size...............: 0.001453 
Standard error............: 0.002864 
Odds ratio (effect size)..: 1.001 
Lower 95% CI..............: 0.996 
Upper 95% CI..............: 1.007 
Z-value...................: 0.507409 
P-value...................: 0.6118676 
Hosmer and Lemeshow r^2...: 0.060548 
Cox and Snell r^2.........: 0.077415 
Nagelkerke's pseudo r^2...: 0.105222 
Sample size of AE DB......: 623 
Sample size of model......: 614 
Missing data %............: 1.444623 

- processing MAC_binned


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, TARGET] + 
    Gender, family = binomial(link = "logit"), data = currentDF)

Coefficients:
        (Intercept)  currentDF[, TARGET]           Gendermale  
          -0.175134             0.004114             0.439912  

Degrees of Freedom: 611 Total (i.e. Null);  609 Residual
Null Deviance:      837.4 
Residual Deviance: 830  AIC: 836

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, TARGET] + 
    Age + Gender + ORdate_epoch, family = binomial(link = "logit"), 
    data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-1.7896  -1.2867   0.9744   1.0453   1.2921  

Coefficients:
                      Estimate Std. Error z value Pr(>|z|)  
(Intercept)          8.397e-01  1.255e+00   0.669   0.5033  
currentDF[, TARGET]  4.233e-03  2.971e-03   1.425   0.1542  
Age                  1.051e-04  9.257e-03   0.011   0.9909  
Gendermale           4.508e-01  1.898e-01   2.374   0.0176 *
ORdate_epoch        -7.641e-05  8.404e-05  -0.909   0.3632  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 837.39  on 611  degrees of freedom
Residual deviance: 829.18  on 607  degrees of freedom
AIC: 839.18

Number of Fisher Scoring iterations: 4

Analyzing in dataset ' AERNASE.clin.hdac9 ' the association of ' HDAC9 ' with ' MAC_binned ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.hdac9 
Score/Exposure/biomarker..: HDAC9 
Trait/outcome.............: MAC_binned 
Effect size...............: 0.004233 
Standard error............: 0.002971 
Odds ratio (effect size)..: 1.004 
Lower 95% CI..............: 0.998 
Upper 95% CI..............: 1.01 
Z-value...................: 1.424928 
P-value...................: 0.154178 
Hosmer and Lemeshow r^2...: 0.009808 
Cox and Snell r^2.........: 0.013331 
Nagelkerke's pseudo r^2...: 0.017883 
Sample size of AE DB......: 623 
Sample size of model......: 612 
Missing data %............: 1.76565 

- processing SMC_binned


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, TARGET] + 
    Age, family = binomial(link = "logit"), data = currentDF)

Coefficients:
        (Intercept)  currentDF[, TARGET]                  Age  
           2.436868            -0.004074            -0.022542  

Degrees of Freedom: 611 Total (i.e. Null);  609 Residual
Null Deviance:      764.5 
Residual Deviance: 756.7    AIC: 762.7

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, TARGET] + 
    Age + Gender + ORdate_epoch, family = binomial(link = "logit"), 
    data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-1.7952  -1.4102   0.8040   0.8926   1.6860  

Coefficients:
                      Estimate Std. Error z value Pr(>|z|)  
(Intercept)          2.992e+00  1.346e+00   2.222   0.0263 *
currentDF[, TARGET] -4.145e-03  2.702e-03  -1.534   0.1249  
Age                 -2.222e-02  1.007e-02  -2.207   0.0273 *
Gendermale          -2.770e-01  2.097e-01  -1.321   0.1866  
ORdate_epoch        -2.685e-05  8.954e-05  -0.300   0.7643  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 764.49  on 611  degrees of freedom
Residual deviance: 754.75  on 607  degrees of freedom
AIC: 764.75

Number of Fisher Scoring iterations: 4

Analyzing in dataset ' AERNASE.clin.hdac9 ' the association of ' HDAC9 ' with ' SMC_binned ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.hdac9 
Score/Exposure/biomarker..: HDAC9 
Trait/outcome.............: SMC_binned 
Effect size...............: -0.004145 
Standard error............: 0.002702 
Odds ratio (effect size)..: 0.996 
Lower 95% CI..............: 0.991 
Upper 95% CI..............: 1.001 
Z-value...................: -1.534329 
P-value...................: 0.1249488 
Hosmer and Lemeshow r^2...: 0.012743 
Cox and Snell r^2.........: 0.015792 
Nagelkerke's pseudo r^2...: 0.022141 
Sample size of AE DB......: 623 
Sample size of model......: 612 
Missing data %............: 1.76565 
cat("Edit the column names...\n")
Edit the column names...
colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
                          "Beta", "s.e.m.",
                          "OR", "low95CI", "up95CI",
                          "Z-value", "P-value", "r^2_l", "r^2_cs", "r^2_nagelkerke", "N", "Model_N", "Perc_Miss")

cat("Correct the variable types...\n")
Correct the variable types...
GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`Z-value` <- as.numeric(GLM.results$`Z-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2_l` <- as.numeric(GLM.results$`r^2_l`)
GLM.results$`r^2_cs` <- as.numeric(GLM.results$`r^2_cs`)
GLM.results$`r^2_nagelkerke` <- as.numeric(GLM.results$`r^2_nagelkerke`)
GLM.results$`N` <- as.numeric(GLM.results$`N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)

# Save the data
cat("Writing results to Excel-file...\n")
Writing results to Excel-file...
### Univariate
write.xlsx(GLM.results,
           file = paste0(OUT_loc, "/",Today,".AERNASE.clin.hdac9.Bin.Uni.",TRAIT_OF_INTEREST,".PlaquePhenotypes.RANK.MODEL1.xlsx"),
           rowNames = FALSE, colNames = TRUE, sheetName = "Bin.Uni.PlaquePheno")

# Removing intermediates
cat("Removing intermediate files...\n")
Removing intermediate files...
rm(TRAIT, trait, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)

Model 2

In this model we correct for Age, Gender, year of surgery, Hypertension status, Diabetes status, current smoker status, lipid-lowering drugs (LLDs), antiplatelet medication, eGFR (MDRD), BMI, MedHx_CVD (combination of CAD history, stroke history, and peripheral interventions), and stenosis.

Here we use the inverse-rank normalized data - visually this is more normally distributed.

Quantitative plaque traits

Analysis of continuous/quantitative plaque traits as a function of plaque HDAC9 expression levels.


GLM.results <- data.frame(matrix(NA, ncol = 15, nrow = 0))
cat("Running linear regression...\n")
Running linear regression...
for (target_of_interest in 1:length(TRAITS.TARGET.RANK)) {
  TARGET = TRAITS.TARGET.RANK[target_of_interest]
  cat(paste0("\nAnalysis of ",TARGET,".\n"))
  for (trait in 1:length(TRAITS.CON.RANK)) {
    TRAIT = TRAITS.CON.RANK[trait]
    cat(paste0("\n- processing ",TRAIT,"\n\n"))
    currentDF <- as.data.frame(AERNASE.clin.hdac9 %>%
      dplyr::select(., TARGET, TRAIT, COVARIATES_M2) %>%
      filter(complete.cases(.))) %>%
      filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
    # for debug
    # print(DT::datatable(currentDF))
    # print(nrow(currentDF))
    # print(str(currentDF))
    ### univariate
    # fit <- lm(currentDF[,TARGET] ~ currentDF[,TRAIT] + Age + Gender + ORdate_year + 
    #             Hypertension.composite + DiabetesStatus + SmokerStatus + 
    #             Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
    #             MedHx_CVD + stenose, 
    #           data = currentDF)
    
    fit <- lm(currentDF[,TARGET] ~ currentDF[,TRAIT] + Age + Gender + ORdate_epoch +
              Hypertension.composite + DiabetesStatus + SmokerStatus + 
              Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
              MedHx_CVD + stenose, 
              data = currentDF) 
    
    model_step <- stepAIC(fit, direction = "both", trace = FALSE)
    print(model_step)
    print(summary(fit))
    
    GLM.results.TEMP <- data.frame(matrix(NA, ncol = 15, nrow = 0))
    GLM.results.TEMP[1,] = GLM.CON(fit, "AERNASE.clin.hdac9", TARGET, TRAIT, verbose = TRUE)
    GLM.results = rbind(GLM.results, GLM.results.TEMP)
  }
}

Analysis of HDAC9.

- processing MAC_rankNorm


Call:
lm(formula = currentDF[, TARGET] ~ 1, data = currentDF)

Coefficients:
(Intercept)  
      28.59  


Call:
lm(formula = currentDF[, TARGET] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, data = currentDF)

Residuals:
   Min     1Q Median     3Q    Max 
-32.79 -18.09  -6.73   7.70 414.49 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)
(Intercept)               -10.091240  38.249933  -0.264    0.792
currentDF[, TRAIT]          0.011511   1.590977   0.007    0.994
Age                         0.161680   0.195565   0.827    0.409
Gendermale                  0.097035   3.568556   0.027    0.978
ORdate_epoch                0.001094   0.001635   0.669    0.504
Hypertension.compositeyes   2.904916   4.567294   0.636    0.525
DiabetesStatusDiabetes     -5.303613   3.776907  -1.404    0.161
SmokerStatusEx-smoker      -0.584559   3.421371  -0.171    0.864
SmokerStatusNever smoked   -3.216563   4.525757  -0.711    0.478
Med.Statin.LLDyes          -0.247286   3.648425  -0.068    0.946
Med.all.antiplateletyes     4.630310   5.206895   0.889    0.374
GFR_MDRD                   -0.021379   0.078478  -0.272    0.785
BMI                        -0.007456   0.407308  -0.018    0.985
MedHx_CVDNo                 0.974358   3.120792   0.312    0.755
stenose50-70%              12.645258  24.734342   0.511    0.609
stenose70-90%               7.002051  24.074940   0.291    0.771
stenose90-99%              11.622272  24.096124   0.482    0.630
stenose100% (Occlusion)     7.828567  29.539505   0.265    0.791

Residual standard error: 33.64 on 507 degrees of freedom
Multiple R-squared:  0.01534,   Adjusted R-squared:  -0.01768 
F-statistic: 0.4646 on 17 and 507 DF,  p-value: 0.9676

Analyzing in dataset ' AERNASE.clin.hdac9 ' the association of ' HDAC9 ' with ' MAC_rankNorm ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.hdac9 
Score/Exposure/biomarker..: HDAC9 
Trait/outcome.............: MAC_rankNorm 
Effect size...............: 0.011511 
Standard error............: 1.590977 
Odds ratio (effect size)..: 1.012 
Lower 95% CI..............: 0.045 
Upper 95% CI..............: 22.87 
T-value...................: 0.007235 
P-value...................: 0.9942299 
R^2.......................: 0.015338 
Adjusted r^2..............: -0.017678 
Sample size of AE DB......: 623 
Sample size of model......: 525 
Missing data %............: 15.73034 

- processing SMC_rankNorm


Call:
lm(formula = currentDF[, TARGET] ~ currentDF[, TRAIT], data = currentDF)

Coefficients:
       (Intercept)  currentDF[, TRAIT]  
            28.427              -4.472  


Call:
lm(formula = currentDF[, TARGET] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, data = currentDF)

Residuals:
   Min     1Q Median     3Q    Max 
-39.66 -17.70  -6.56   6.78 413.59 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)   
(Intercept)               -0.7847801 38.2114545  -0.021  0.98362   
currentDF[, TRAIT]        -4.3670090  1.6496545  -2.647  0.00837 **
Age                        0.1153929  0.1952382   0.591  0.55476   
Gendermale                -1.4107994  3.5852149  -0.394  0.69411   
ORdate_epoch               0.0005486  0.0016237   0.338  0.73561   
Hypertension.compositeyes  2.5480563  4.5364002   0.562  0.57458   
DiabetesStatusDiabetes    -4.4946625  3.7607644  -1.195  0.23259   
SmokerStatusEx-smoker     -0.4860758  3.4024177  -0.143  0.88646   
SmokerStatusNever smoked  -3.7090935  4.4983490  -0.825  0.41002   
Med.Statin.LLDyes         -0.5002184  3.6311515  -0.138  0.89049   
Med.all.antiplateletyes    4.1991048  5.1746758   0.811  0.41748   
GFR_MDRD                  -0.0021769  0.0781408  -0.028  0.97779   
BMI                       -0.0058534  0.4045985  -0.014  0.98846   
MedHx_CVDNo                1.5037054  3.1064665   0.484  0.62856   
stenose50-70%             13.3478962 24.5399585   0.544  0.58673   
stenose70-90%              8.2364390 23.8706266   0.345  0.73020   
stenose90-99%             13.1154314 23.9133841   0.548  0.58362   
stenose100% (Occlusion)    9.3401996 29.3473745   0.318  0.75042   
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 33.41 on 506 degrees of freedom
Multiple R-squared:  0.02898,   Adjusted R-squared:  -0.003638 
F-statistic: 0.8885 on 17 and 506 DF,  p-value: 0.5881

Analyzing in dataset ' AERNASE.clin.hdac9 ' the association of ' HDAC9 ' with ' SMC_rankNorm ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.hdac9 
Score/Exposure/biomarker..: HDAC9 
Trait/outcome.............: SMC_rankNorm 
Effect size...............: -4.367009 
Standard error............: 1.649655 
Odds ratio (effect size)..: 0.013 
Lower 95% CI..............: 0.001 
Upper 95% CI..............: 0.322 
T-value...................: -2.647226 
P-value...................: 0.008368544 
R^2.......................: 0.028985 
Adjusted r^2..............: -0.003638 
Sample size of AE DB......: 623 
Sample size of model......: 524 
Missing data %............: 15.89085 

- processing MAC_SMC_ratio_rank


Call:
lm(formula = currentDF[, TARGET] ~ currentDF[, TRAIT] + DiabetesStatus, 
    data = currentDF)

Coefficients:
           (Intercept)      currentDF[, TRAIT]  DiabetesStatusDiabetes  
                29.591                  -3.515                  -5.360  


Call:
lm(formula = currentDF[, TARGET] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, data = currentDF)

Residuals:
   Min     1Q Median     3Q    Max 
-36.46 -16.97  -6.66   8.36 410.03 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)   
(Intercept)               -20.439631  38.126367  -0.536  0.59212   
currentDF[, TRAIT]         -4.066568   1.522841  -2.670  0.00782 **
Age                         0.164831   0.194296   0.848  0.39664   
Gendermale                 -0.561417   3.549429  -0.158  0.87439   
ORdate_epoch                0.001597   0.001614   0.989  0.32300   
Hypertension.compositeyes   3.311944   4.537894   0.730  0.46582   
DiabetesStatusDiabetes     -5.674795   3.754089  -1.512  0.13125   
SmokerStatusEx-smoker      -0.964921   3.402408  -0.284  0.77683   
SmokerStatusNever smoked   -4.245343   4.510008  -0.941  0.34699   
Med.Statin.LLDyes           0.004525   3.625219   0.001  0.99900   
Med.all.antiplateletyes     5.475340   5.184680   1.056  0.29144   
GFR_MDRD                   -0.020198   0.077888  -0.259  0.79549   
BMI                        -0.069206   0.405156  -0.171  0.86444   
MedHx_CVDNo                 1.327313   3.103312   0.428  0.66905   
stenose50-70%              16.043342  24.567862   0.653  0.51404   
stenose70-90%              11.067584  23.912034   0.463  0.64367   
stenose90-99%              16.311118  23.965602   0.681  0.49643   
stenose100% (Occlusion)    11.942968  29.378022   0.407  0.68453   
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 33.41 on 506 degrees of freedom
Multiple R-squared:  0.02922,   Adjusted R-squared:  -0.003397 
F-statistic: 0.8958 on 17 and 506 DF,  p-value: 0.5792

Analyzing in dataset ' AERNASE.clin.hdac9 ' the association of ' HDAC9 ' with ' MAC_SMC_ratio_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.hdac9 
Score/Exposure/biomarker..: HDAC9 
Trait/outcome.............: MAC_SMC_ratio_rank 
Effect size...............: -4.066568 
Standard error............: 1.522841 
Odds ratio (effect size)..: 0.017 
Lower 95% CI..............: 0.001 
Upper 95% CI..............: 0.339 
T-value...................: -2.670383 
P-value...................: 0.007820257 
R^2.......................: 0.029218 
Adjusted r^2..............: -0.003397 
Sample size of AE DB......: 623 
Sample size of model......: 524 
Missing data %............: 15.89085 

- processing VesselDensity_rankNorm


Call:
lm(formula = currentDF[, TARGET] ~ DiabetesStatus, data = currentDF)

Coefficients:
           (Intercept)  DiabetesStatusDiabetes  
                30.094                  -6.184  


Call:
lm(formula = currentDF[, TARGET] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, data = currentDF)

Residuals:
   Min     1Q Median     3Q    Max 
-33.84 -18.19  -6.70   8.32 414.11 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)
(Intercept)               -13.375880  40.431373  -0.331    0.741
currentDF[, TRAIT]         -0.198401   1.717763  -0.115    0.908
Age                         0.146617   0.207997   0.705    0.481
Gendermale                  0.040816   3.759758   0.011    0.991
ORdate_epoch                0.001486   0.001739   0.855    0.393
Hypertension.compositeyes   0.910538   4.861182   0.187    0.851
DiabetesStatusDiabetes     -6.394737   4.140531  -1.544    0.123
SmokerStatusEx-smoker      -0.647605   3.642397  -0.178    0.859
SmokerStatusNever smoked   -2.855860   4.856549  -0.588    0.557
Med.Statin.LLDyes          -1.765814   3.849786  -0.459    0.647
Med.all.antiplateletyes     4.990184   5.525657   0.903    0.367
GFR_MDRD                   -0.038742   0.083117  -0.466    0.641
BMI                         0.090860   0.435965   0.208    0.835
MedHx_CVDNo                 1.807646   3.312055   0.546    0.585
stenose50-70%              12.237496  25.415228   0.482    0.630
stenose70-90%               7.626824  24.609474   0.310    0.757
stenose90-99%              12.192396  24.646142   0.495    0.621
stenose100% (Occlusion)     7.967067  30.242214   0.263    0.792
stenose50-99%               3.506790  42.857537   0.082    0.935

Residual standard error: 34.39 on 473 degrees of freedom
Multiple R-squared:  0.0167,    Adjusted R-squared:  -0.02072 
F-statistic: 0.4463 on 18 and 473 DF,  p-value: 0.9771

Analyzing in dataset ' AERNASE.clin.hdac9 ' the association of ' HDAC9 ' with ' VesselDensity_rankNorm ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.hdac9 
Score/Exposure/biomarker..: HDAC9 
Trait/outcome.............: VesselDensity_rankNorm 
Effect size...............: -0.198401 
Standard error............: 1.717763 
Odds ratio (effect size)..: 0.82 
Lower 95% CI..............: 0.028 
Upper 95% CI..............: 23.77 
T-value...................: -0.1155 
P-value...................: 0.9080981 
R^2.......................: 0.0167 
Adjusted r^2..............: -0.020719 
Sample size of AE DB......: 623 
Sample size of model......: 492 
Missing data %............: 21.02729 
cat("Edit the column names...\n")
Edit the column names...
colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
                          "Beta", "s.e.m.",
                          "OR", "low95CI", "up95CI",
                          "T-value", "P-value", "r^2", "r^2_adj", "N", "Model_N", "Perc_Miss")

cat("Correct the variable types...\n")
Correct the variable types...
GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`T-value` <- as.numeric(GLM.results$`T-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2` <- as.numeric(GLM.results$`r^2`)
GLM.results$`r^2_adj` <- as.numeric(GLM.results$`r^2_adj`)
GLM.results$`N` <- as.numeric(GLM.results$`N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)

# Save the data
cat("Writing results to Excel-file...\n")
Writing results to Excel-file...
### Univariate
library(openxlsx)
write.xlsx(GLM.results,
           file = paste0(OUT_loc, "/",Today,".AERNASE.clin.hdac9.Con.Multi.",TRAIT_OF_INTEREST,".PlaquePhenotypes.RANK.MODEL2.xlsx"),
           rowNames = FALSE, colNames = TRUE, sheetName = "Con.Multi.PlaquePheno")
# Removing intermediates
cat("Removing intermediate files...\n")
Removing intermediate files...
rm(TRAIT, trait, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)

Binary plaque traits

Analysis of binary plaque traits as a function of plaque MCP1 levels.


GLM.results <- data.frame(matrix(NA, ncol = 16, nrow = 0))
for (target_of_interest in 1:length(TRAITS.TARGET.RANK)) {
  TARGET = TRAITS.TARGET.RANK[target_of_interest]
  cat(paste0("\nAnalysis of ",TARGET,".\n"))
  for (trait in 1:length(TRAITS.BIN)) {
    TRAIT = TRAITS.BIN[trait]
    cat(paste0("\n- processing ",TRAIT,"\n\n"))
    currentDF <- as.data.frame(AERNASE.clin.hdac9 %>%
      dplyr::select(., TARGET, TRAIT, COVARIATES_M2) %>%
      filter(complete.cases(.))) %>%
      filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
    # for debug
    # print(DT::datatable(currentDF))
    # print(nrow(currentDF))
    # print(str(currentDF))
    # print(class(currentDF[,TRAIT]))
    ### univariate
    # fit <- glm(as.factor(currentDF[,TRAIT]) ~ currentDF[,TARGET] + Age + Gender + ORdate_year + 
    #             Hypertension.composite + DiabetesStatus + SmokerStatus + 
    #             Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
    #             MedHx_CVD + stenose, 
    #           data  =  currentDF, family = binomial(link = "logit"))
    
    fit <- glm(as.factor(currentDF[,TRAIT]) ~ currentDF[,TARGET] + Age + Gender + ORdate_epoch + 
                Hypertension.composite + DiabetesStatus + SmokerStatus + 
                Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
                MedHx_CVD + stenose, 
              data  =  currentDF, family = binomial(link = "logit"))
    
    model_step <- stepAIC(fit, direction = "both", trace = FALSE)
    print(model_step)
    print(summary(fit))
    
    GLM.results.TEMP <- data.frame(matrix(NA, ncol = 16, nrow = 0))
    GLM.results.TEMP[1,] = GLM.BIN(fit, "AERNASE.clin.hdac9", TARGET, TRAIT, verbose = TRUE)
    GLM.results = rbind(GLM.results, GLM.results.TEMP)
  }
}

Analysis of HDAC9.

- processing CalcificationPlaque


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, TARGET] + 
    Gender + ORdate_epoch + Hypertension.composite, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
              (Intercept)        currentDF[, TARGET]                 Gendermale               ORdate_epoch  Hypertension.compositeyes  
                6.8650431                  0.0056556                 -0.3311542                 -0.0005261                  0.4837455  

Degrees of Freedom: 529 Total (i.e. Null);  525 Residual
Null Deviance:      733.8 
Residual Deviance: 691.8    AIC: 701.8

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, TARGET] + 
    Age + Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, family = binomial(link = "logit"), 
    data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-1.7749  -1.0842   0.7152   1.0645   1.7710  

Coefficients:
                            Estimate Std. Error z value Pr(>|z|)    
(Intercept)                7.263e+00  2.394e+00   3.034  0.00241 ** 
currentDF[, TARGET]        5.323e-03  3.394e-03   1.568  0.11686    
Age                       -4.408e-03  1.212e-02  -0.364  0.71604    
Gendermale                -3.524e-01  2.227e-01  -1.583  0.11346    
ORdate_epoch              -4.677e-04  1.007e-04  -4.645 3.41e-06 ***
Hypertension.compositeyes  4.955e-01  2.852e-01   1.737  0.08231 .  
DiabetesStatusDiabetes    -8.297e-02  2.322e-01  -0.357  0.72089    
SmokerStatusEx-smoker     -2.505e-02  2.124e-01  -0.118  0.90611    
SmokerStatusNever smoked  -2.432e-01  2.810e-01  -0.865  0.38683    
Med.Statin.LLDyes         -1.093e-01  2.254e-01  -0.485  0.62760    
Med.all.antiplateletyes   -4.505e-02  3.217e-01  -0.140  0.88862    
GFR_MDRD                  -4.935e-03  4.848e-03  -1.018  0.30866    
BMI                       -4.248e-04  2.524e-02  -0.017  0.98657    
MedHx_CVDNo                2.581e-02  1.940e-01   0.133  0.89418    
stenose50-70%             -4.494e-01  1.542e+00  -0.291  0.77079    
stenose70-90%             -5.059e-01  1.499e+00  -0.338  0.73574    
stenose90-99%             -1.327e-01  1.502e+00  -0.088  0.92956    
stenose100% (Occlusion)    6.675e-01  1.907e+00   0.350  0.72638    
stenose50-99%             -1.218e+01  5.354e+02  -0.023  0.98185    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 733.82  on 529  degrees of freedom
Residual deviance: 684.37  on 511  degrees of freedom
AIC: 722.37

Number of Fisher Scoring iterations: 12

Analyzing in dataset ' AERNASE.clin.hdac9 ' the association of ' HDAC9 ' with ' CalcificationPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.hdac9 
Score/Exposure/biomarker..: HDAC9 
Trait/outcome.............: CalcificationPlaque 
Effect size...............: 0.005323 
Standard error............: 0.003394 
Odds ratio (effect size)..: 1.005 
Lower 95% CI..............: 0.999 
Upper 95% CI..............: 1.012 
Z-value...................: 1.568105 
P-value...................: 0.1168566 
Hosmer and Lemeshow r^2...: 0.067384 
Cox and Snell r^2.........: 0.089078 
Nagelkerke's pseudo r^2...: 0.118839 
Sample size of AE DB......: 623 
Sample size of model......: 530 
Missing data %............: 14.92777 

- processing CollagenPlaque


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ SmokerStatus + 
    Med.all.antiplatelet, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
             (Intercept)     SmokerStatusEx-smoker  SmokerStatusNever smoked   Med.all.antiplateletyes  
                 1.08806                  -0.08434                  -0.66574                   0.61326  

Degrees of Freedom: 528 Total (i.e. Null);  525 Residual
Null Deviance:      510 
Residual Deviance: 501.9    AIC: 509.9

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, TARGET] + 
    Age + Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, family = binomial(link = "logit"), 
    data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.1896   0.4820   0.5659   0.6630   1.1019  

Coefficients:
                            Estimate Std. Error z value Pr(>|z|)  
(Intercept)                1.320e+01  1.009e+03   0.013   0.9896  
currentDF[, TARGET]       -3.871e-03  3.017e-03  -1.283   0.1994  
Age                        1.035e-02  1.506e-02   0.687   0.4919  
Gendermale                -1.286e-02  2.757e-01  -0.047   0.9628  
ORdate_epoch               8.164e-05  1.247e-04   0.655   0.5126  
Hypertension.compositeyes -1.573e-01  3.621e-01  -0.434   0.6639  
DiabetesStatusDiabetes     1.813e-04  2.959e-01   0.001   0.9995  
SmokerStatusEx-smoker     -1.208e-01  2.755e-01  -0.438   0.6611  
SmokerStatusNever smoked  -7.115e-01  3.272e-01  -2.174   0.0297 *
Med.Statin.LLDyes         -1.921e-01  2.866e-01  -0.670   0.5026  
Med.all.antiplateletyes    7.816e-01  3.579e-01   2.184   0.0290 *
GFR_MDRD                  -1.859e-03  6.119e-03  -0.304   0.7613  
BMI                        2.833e-02  3.252e-02   0.871   0.3836  
MedHx_CVDNo               -1.020e-01  2.415e-01  -0.422   0.6728  
stenose50-70%             -1.506e+01  1.009e+03  -0.015   0.9881  
stenose70-90%             -1.429e+01  1.009e+03  -0.014   0.9887  
stenose90-99%             -1.403e+01  1.009e+03  -0.014   0.9889  
stenose100% (Occlusion)    2.998e-01  1.233e+03   0.000   0.9998  
stenose50-99%             -6.314e-01  1.771e+03   0.000   0.9997  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 510.02  on 528  degrees of freedom
Residual deviance: 490.30  on 510  degrees of freedom
AIC: 528.3

Number of Fisher Scoring iterations: 14

Analyzing in dataset ' AERNASE.clin.hdac9 ' the association of ' HDAC9 ' with ' CollagenPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.hdac9 
Score/Exposure/biomarker..: HDAC9 
Trait/outcome.............: CollagenPlaque 
Effect size...............: -0.003871 
Standard error............: 0.003017 
Odds ratio (effect size)..: 0.996 
Lower 95% CI..............: 0.99 
Upper 95% CI..............: 1.002 
Z-value...................: -1.283273 
P-value...................: 0.1993966 
Hosmer and Lemeshow r^2...: 0.038651 
Cox and Snell r^2.........: 0.036579 
Nagelkerke's pseudo r^2...: 0.059124 
Sample size of AE DB......: 623 
Sample size of model......: 529 
Missing data %............: 15.08828 

- processing Fat10Perc


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, TARGET] + 
    Gender + ORdate_epoch, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
        (Intercept)  currentDF[, TARGET]           Gendermale         ORdate_epoch  
          8.1301039            0.0203375            0.6845070           -0.0005857  

Degrees of Freedom: 529 Total (i.e. Null);  526 Residual
Null Deviance:      583.7 
Residual Deviance: 535.6    AIC: 543.6

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, TARGET] + 
    Age + Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, family = binomial(link = "logit"), 
    data = currentDF)

Deviance Residuals: 
     Min        1Q    Median        3Q       Max  
-2.63164   0.09199   0.55976   0.74799   1.36449  

Coefficients:
                            Estimate Std. Error z value Pr(>|z|)    
(Intercept)                2.200e+01  6.125e+02   0.036  0.97135    
currentDF[, TARGET]        1.990e-02  6.053e-03   3.288  0.00101 ** 
Age                        2.015e-02  1.432e-02   1.407  0.15930    
Gendermale                 7.426e-01  2.527e-01   2.939  0.00329 ** 
ORdate_epoch              -6.930e-04  1.205e-04  -5.750 8.93e-09 ***
Hypertension.compositeyes -1.126e-02  3.375e-01  -0.033  0.97339    
DiabetesStatusDiabetes    -1.478e-01  2.727e-01  -0.542  0.58786    
SmokerStatusEx-smoker     -1.207e-01  2.530e-01  -0.477  0.63329    
SmokerStatusNever smoked  -1.047e-01  3.328e-01  -0.315  0.75298    
Med.Statin.LLDyes          1.378e-01  2.720e-01   0.507  0.61249    
Med.all.antiplateletyes    4.566e-01  3.636e-01   1.256  0.20928    
GFR_MDRD                   4.535e-03  5.812e-03   0.780  0.43524    
BMI                       -3.618e-02  2.910e-02  -1.243  0.21382    
MedHx_CVDNo                6.926e-02  2.296e-01   0.302  0.76288    
stenose50-70%             -1.266e+01  6.125e+02  -0.021  0.98351    
stenose70-90%             -1.353e+01  6.125e+02  -0.022  0.98237    
stenose90-99%             -1.378e+01  6.125e+02  -0.022  0.98205    
stenose100% (Occlusion)   -1.429e+01  6.125e+02  -0.023  0.98138    
stenose50-99%             -2.813e+01  1.074e+03  -0.026  0.97911    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 583.68  on 529  degrees of freedom
Residual deviance: 521.56  on 511  degrees of freedom
AIC: 559.56

Number of Fisher Scoring iterations: 13

Analyzing in dataset ' AERNASE.clin.hdac9 ' the association of ' HDAC9 ' with ' Fat10Perc ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.hdac9 
Score/Exposure/biomarker..: HDAC9 
Trait/outcome.............: Fat10Perc 
Effect size...............: 0.019905 
Standard error............: 0.006053 
Odds ratio (effect size)..: 1.02 
Lower 95% CI..............: 1.008 
Upper 95% CI..............: 1.032 
Z-value...................: 3.28842 
P-value...................: 0.001007514 
Hosmer and Lemeshow r^2...: 0.10643 
Cox and Snell r^2.........: 0.110601 
Nagelkerke's pseudo r^2...: 0.165681 
Sample size of AE DB......: 623 
Sample size of model......: 530 
Missing data %............: 14.92777 

- processing IPH


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ Gender + ORdate_epoch + 
    MedHx_CVD, family = binomial(link = "logit"), data = currentDF)

Coefficients:
 (Intercept)    Gendermale  ORdate_epoch   MedHx_CVDNo  
   7.6697898     0.3451109    -0.0005425    -0.2775456  

Degrees of Freedom: 528 Total (i.e. Null);  525 Residual
Null Deviance:      705.4 
Residual Deviance: 667.2    AIC: 675.2

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, TARGET] + 
    Age + Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, family = binomial(link = "logit"), 
    data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.0497  -1.1745   0.7199   0.9686   1.7180  

Coefficients:
                            Estimate Std. Error z value Pr(>|z|)    
(Intercept)                2.208e+01  5.807e+02   0.038   0.9697    
currentDF[, TARGET]        1.632e-03  2.994e-03   0.545   0.5857    
Age                        2.692e-03  1.244e-02   0.216   0.8288    
Gendermale                 3.744e-01  2.241e-01   1.670   0.0949 .  
ORdate_epoch              -5.413e-04  1.034e-04  -5.237 1.63e-07 ***
Hypertension.compositeyes -2.178e-01  2.913e-01  -0.748   0.4547    
DiabetesStatusDiabetes    -1.030e-01  2.386e-01  -0.432   0.6660    
SmokerStatusEx-smoker      1.670e-02  2.178e-01   0.077   0.9389    
SmokerStatusNever smoked   1.959e-01  2.888e-01   0.678   0.4977    
Med.Statin.LLDyes         -2.582e-01  2.360e-01  -1.094   0.2739    
Med.all.antiplateletyes    2.576e-01  3.278e-01   0.786   0.4320    
GFR_MDRD                  -2.910e-03  4.981e-03  -0.584   0.5591    
BMI                        4.573e-03  2.580e-02   0.177   0.8593    
MedHx_CVDNo               -3.563e-01  1.972e-01  -1.807   0.0707 .  
stenose50-70%             -1.444e+01  5.807e+02  -0.025   0.9802    
stenose70-90%             -1.448e+01  5.807e+02  -0.025   0.9801    
stenose90-99%             -1.436e+01  5.807e+02  -0.025   0.9803    
stenose100% (Occlusion)   -1.517e+01  5.807e+02  -0.026   0.9792    
stenose50-99%             -2.844e+01  1.057e+03  -0.027   0.9785    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 705.43  on 528  degrees of freedom
Residual deviance: 658.40  on 510  degrees of freedom
AIC: 696.4

Number of Fisher Scoring iterations: 13

Analyzing in dataset ' AERNASE.clin.hdac9 ' the association of ' HDAC9 ' with ' IPH ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.hdac9 
Score/Exposure/biomarker..: HDAC9 
Trait/outcome.............: IPH 
Effect size...............: 0.001632 
Standard error............: 0.002994 
Odds ratio (effect size)..: 1.002 
Lower 95% CI..............: 0.996 
Upper 95% CI..............: 1.008 
Z-value...................: 0.545074 
P-value...................: 0.5857023 
Hosmer and Lemeshow r^2...: 0.066668 
Cox and Snell r^2.........: 0.085065 
Nagelkerke's pseudo r^2...: 0.115507 
Sample size of AE DB......: 623 
Sample size of model......: 529 
Missing data %............: 15.08828 

- processing MAC_binned


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ Gender + BMI, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
(Intercept)   Gendermale          BMI  
    0.96605      0.42042     -0.03957  

Degrees of Freedom: 526 Total (i.e. Null);  524 Residual
Null Deviance:      723.5 
Residual Deviance: 716.7    AIC: 722.7

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, TARGET] + 
    Age + Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, family = binomial(link = "logit"), 
    data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-1.7916  -1.2337   0.9035   1.0567   1.5594  

Coefficients:
                            Estimate Std. Error z value Pr(>|z|)  
(Intercept)                1.901e+00  2.293e+00   0.829   0.4071  
currentDF[, TARGET]        3.554e-03  3.066e-03   1.159   0.2463  
Age                       -5.878e-04  1.186e-02  -0.050   0.9605  
Gendermale                 4.628e-01  2.158e-01   2.145   0.0320 *
ORdate_epoch              -9.757e-05  9.660e-05  -1.010   0.3125  
Hypertension.compositeyes  1.367e-01  2.746e-01   0.498   0.6188  
DiabetesStatusDiabetes    -1.371e-01  2.298e-01  -0.597   0.5508  
SmokerStatusEx-smoker      9.321e-02  2.078e-01   0.449   0.6538  
SmokerStatusNever smoked   1.328e-01  2.745e-01   0.484   0.6286  
Med.Statin.LLDyes          2.739e-01  2.198e-01   1.246   0.2127  
Med.all.antiplateletyes   -1.075e-01  3.174e-01  -0.339   0.7348  
GFR_MDRD                  -2.536e-03  4.770e-03  -0.532   0.5950  
BMI                       -4.501e-02  2.487e-02  -1.809   0.0704 .
MedHx_CVDNo               -5.758e-02  1.895e-01  -0.304   0.7612  
stenose50-70%              4.294e-02  1.473e+00   0.029   0.9767  
stenose70-90%              5.184e-01  1.431e+00   0.362   0.7171  
stenose90-99%              3.018e-01  1.433e+00   0.211   0.8332  
stenose100% (Occlusion)   -1.287e-01  1.760e+00  -0.073   0.9417  
stenose50-99%             -1.303e+01  5.354e+02  -0.024   0.9806  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 723.50  on 526  degrees of freedom
Residual deviance: 706.23  on 508  degrees of freedom
AIC: 744.23

Number of Fisher Scoring iterations: 12

Analyzing in dataset ' AERNASE.clin.hdac9 ' the association of ' HDAC9 ' with ' MAC_binned ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.hdac9 
Score/Exposure/biomarker..: HDAC9 
Trait/outcome.............: MAC_binned 
Effect size...............: 0.003554 
Standard error............: 0.003066 
Odds ratio (effect size)..: 1.004 
Lower 95% CI..............: 0.998 
Upper 95% CI..............: 1.01 
Z-value...................: 1.15947 
P-value...................: 0.2462649 
Hosmer and Lemeshow r^2...: 0.023877 
Cox and Snell r^2.........: 0.032249 
Nagelkerke's pseudo r^2...: 0.043193 
Sample size of AE DB......: 623 
Sample size of model......: 527 
Missing data %............: 15.40931 

- processing SMC_binned


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, TARGET] + 
    Age + Gender + MedHx_CVD, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
        (Intercept)  currentDF[, TARGET]                  Age           Gendermale          MedHx_CVDNo  
            2.11537             -0.00535             -0.01568             -0.34967              0.43278  

Degrees of Freedom: 527 Total (i.e. Null);  523 Residual
Null Deviance:      659 
Residual Deviance: 645.5    AIC: 655.5

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, TARGET] + 
    Age + Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, family = binomial(link = "logit"), 
    data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.0716  -1.2985   0.7195   0.8876   1.7432  

Coefficients:
                            Estimate Std. Error z value Pr(>|z|)  
(Intercept)               -3.770e-01  2.415e+00  -0.156   0.8759  
currentDF[, TARGET]       -6.093e-03  2.918e-03  -2.088   0.0368 *
Age                       -1.910e-02  1.309e-02  -1.459   0.1446  
Gendermale                -3.831e-01  2.439e-01  -1.571   0.1162  
ORdate_epoch               1.153e-04  1.064e-04   1.083   0.2786  
Hypertension.compositeyes  1.180e-01  2.978e-01   0.396   0.6918  
DiabetesStatusDiabetes    -1.661e-01  2.449e-01  -0.678   0.4975  
SmokerStatusEx-smoker     -4.962e-03  2.262e-01  -0.022   0.9825  
SmokerStatusNever smoked  -4.407e-01  2.911e-01  -1.514   0.1301  
Med.Statin.LLDyes          5.922e-02  2.375e-01   0.249   0.8031  
Med.all.antiplateletyes   -2.639e-01  3.497e-01  -0.755   0.4504  
GFR_MDRD                  -3.020e-03  5.139e-03  -0.588   0.5568  
BMI                        3.035e-02  2.771e-02   1.095   0.2734  
MedHx_CVDNo                4.809e-01  2.101e-01   2.289   0.0221 *
stenose50-70%              2.059e-01  1.475e+00   0.140   0.8890  
stenose70-90%              6.309e-01  1.433e+00   0.440   0.6597  
stenose90-99%              1.122e+00  1.438e+00   0.781   0.4350  
stenose100% (Occlusion)   -5.214e-02  1.773e+00  -0.029   0.9765  
stenose50-99%              1.380e+01  5.354e+02   0.026   0.9794  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 658.99  on 527  degrees of freedom
Residual deviance: 629.87  on 509  degrees of freedom
AIC: 667.87

Number of Fisher Scoring iterations: 12

Analyzing in dataset ' AERNASE.clin.hdac9 ' the association of ' HDAC9 ' with ' SMC_binned ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.hdac9 
Score/Exposure/biomarker..: HDAC9 
Trait/outcome.............: SMC_binned 
Effect size...............: -0.006093 
Standard error............: 0.002918 
Odds ratio (effect size)..: 0.994 
Lower 95% CI..............: 0.988 
Upper 95% CI..............: 1 
Z-value...................: -2.087993 
P-value...................: 0.03679844 
Hosmer and Lemeshow r^2...: 0.044176 
Cox and Snell r^2.........: 0.053642 
Nagelkerke's pseudo r^2...: 0.075241 
Sample size of AE DB......: 623 
Sample size of model......: 528 
Missing data %............: 15.2488 
cat("Edit the column names...\n")
Edit the column names...
colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
                          "Beta", "s.e.m.",
                          "OR", "low95CI", "up95CI",
                          "Z-value", "P-value", "r^2_l", "r^2_cs", "r^2_nagelkerke", "N", "Model_N", "Perc_Miss")

cat("Correct the variable types...\n")
Correct the variable types...
GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`Z-value` <- as.numeric(GLM.results$`Z-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2_l` <- as.numeric(GLM.results$`r^2_l`)
GLM.results$`r^2_cs` <- as.numeric(GLM.results$`r^2_cs`)
GLM.results$`r^2_nagelkerke` <- as.numeric(GLM.results$`r^2_nagelkerke`)
GLM.results$`N` <- as.numeric(GLM.results$`N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)

# Save the data
cat("Writing results to Excel-file...\n")
Writing results to Excel-file...
### Univariate
write.xlsx(GLM.results,
           file = paste0(OUT_loc, "/",Today,".AERNASE.clin.hdac9.Bin.Multi.",TRAIT_OF_INTEREST,".PlaquePhenotypes.RANK.MODEL2.xlsx"),
           rowNames = FALSE, colNames = TRUE, sheetName = "Bin.Multi.PlaquePheno")

# Removing intermediates
cat("Removing intermediate files...\n")
Removing intermediate files...
rm(TRAIT, trait, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)

B. Cross-sectional analysis symptoms

We will perform a cross-sectional analysis between plaque HDAC9 expression levels and the ‘clinical status’ of the plaque in terms of presence of patients’ symptoms (symptomatic vs. asymptomatic). The symptoms of interest are:

  • stroke
  • TIA
  • retinal infarction
  • amaurosis fugax
  • asymptomatic

Model 1

In this model we correct for Age, Gender, and year of surgery.

GLM.results <- data.frame(matrix(NA, ncol = 16, nrow = 0))
for (target_of_interest in 1:length(TRAITS.TARGET.RANK)) {
  TARGET = TRAITS.TARGET.RANK[target_of_interest]
  cat(paste0("\nAnalysis of ",TARGET,".\n"))
  TRAIT = "AsymptSympt"
    cat(paste0("\n- processing ",TRAIT,"\n\n"))
    currentDF <- as.data.frame(AERNASE.clin.hdac9 %>%
      dplyr::select(., TARGET, TRAIT, COVARIATES_M1) %>%
      filter(complete.cases(.))) %>%
      filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
    # for debug
    # print(DT::datatable(currentDF))
    # print(nrow(currentDF))
    # print(str(currentDF))
    # print(class(currentDF[,TRAIT]))
    ### univariate
     # + Hypertension.composite + DiabetesStatus + SmokerCurrent + 
     #            Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
     #            CAD_history + Stroke_history + Peripheral.interv + stenose
    # fit <- glm(as.factor(currentDF[,TRAIT]) ~ currentDF[,TARGET] + Age + Gender + ORdate_year, 
    #           data  =  currentDF, family = binomial(link = "logit"))

    fit <- glm(as.factor(currentDF[,TRAIT]) ~ currentDF[,TARGET] + Age + Gender + ORdate_epoch, 
              data  =  currentDF, family = binomial(link = "logit"))
    
    model_step <- stepAIC(fit, direction = "both", trace = FALSE)
    print(model_step)
    print(summary(fit))
    
    GLM.results.TEMP <- data.frame(matrix(NA, ncol = 16, nrow = 0))
    GLM.results.TEMP[1,] = GLM.BIN(fit, "AERNASE.clin.hdac9", TARGET, TRAIT, verbose = TRUE)
    GLM.results = rbind(GLM.results, GLM.results.TEMP)
  }

Analysis of HDAC9.

- processing AsymptSympt


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, TARGET] + 
    Age, family = binomial(link = "logit"), data = currentDF)

Coefficients:
        (Intercept)  currentDF[, TARGET]                  Age  
           -1.21372              0.01044              0.04260  

Degrees of Freedom: 622 Total (i.e. Null);  620 Residual
Null Deviance:      477.7 
Residual Deviance: 463.5    AIC: 469.5

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, TARGET] + 
    Age + Gender + ORdate_epoch, family = binomial(link = "logit"), 
    data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.5580   0.3828   0.4791   0.5704   0.9491  

Coefficients:
                      Estimate Std. Error z value Pr(>|z|)   
(Intercept)         -1.961e+00  1.820e+00  -1.078  0.28117   
currentDF[, TARGET]  9.840e-03  5.996e-03   1.641  0.10078   
Age                  4.313e-02  1.363e-02   3.164  0.00155 **
Gendermale          -4.060e-01  3.070e-01  -1.322  0.18606   
ORdate_epoch         7.727e-05  1.186e-04   0.652  0.51458   
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 477.66  on 622  degrees of freedom
Residual deviance: 461.27  on 618  degrees of freedom
AIC: 471.27

Number of Fisher Scoring iterations: 5

Analyzing in dataset ' AERNASE.clin.hdac9 ' the association of ' HDAC9 ' with ' AsymptSympt ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.hdac9 
Score/Exposure/biomarker..: HDAC9 
Trait/outcome.............: AsymptSympt 
Effect size...............: 0.00984 
Standard error............: 0.005996 
Odds ratio (effect size)..: 1.01 
Lower 95% CI..............: 0.998 
Upper 95% CI..............: 1.022 
Z-value...................: 1.64107 
P-value...................: 0.1007828 
Hosmer and Lemeshow r^2...: 0.03432 
Cox and Snell r^2.........: 0.02597 
Nagelkerke's pseudo r^2...: 0.0485 
Sample size of AE DB......: 623 
Sample size of model......: 623 
Missing data %............: 0 
cat("Edit the column names...\n")
Edit the column names...
colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
                          "Beta", "s.e.m.",
                          "OR", "low95CI", "up95CI",
                          "Z-value", "P-value", "r^2_l", "r^2_cs", "r^2_nagelkerke", "N", "Model_N", "Perc_Miss")

cat("Correct the variable types...\n")
Correct the variable types...
GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`Z-value` <- as.numeric(GLM.results$`Z-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2_l` <- as.numeric(GLM.results$`r^2_l`)
GLM.results$`r^2_cs` <- as.numeric(GLM.results$`r^2_cs`)
GLM.results$`r^2_nagelkerke` <- as.numeric(GLM.results$`r^2_nagelkerke`)
GLM.results$`N` <- as.numeric(GLM.results$`N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)

# Save the data
cat("Writing results to Excel-file...\n")
Writing results to Excel-file...
### Univariate
write.xlsx(GLM.results,
           file = paste0(OUT_loc, "/",Today,".AERNASE.clin.hdac9.Bin.Uni.",TRAIT_OF_INTEREST,".RANK.Symptoms.MODEL1.xlsx"),
           rowNames = FALSE, colNames = TRUE, sheetName = "Bin.Uni.Symptoms")

# Removing intermediates
cat("Removing intermediate files...\n")
Removing intermediate files...
rm(TRAIT, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)

Model 2

In this model we correct for Age, Gender, Hypertension status, Diabetes status, current smoker status, lipid-lowering drugs (LLDs), antiplatelet medication, eGFR (MDRD), BMI, MedHx_CVD (combination of CAD history, stroke history, and peripheral interventions), and stenosis..


GLM.results <- data.frame(matrix(NA, ncol = 16, nrow = 0))
for (target_of_interest in 1:length(TRAITS.TARGET.RANK)) {
  TARGET = TRAITS.TARGET.RANK[target_of_interest]
  cat(paste0("\nAnalysis of ",TARGET,".\n"))
  TRAIT = "AsymptSympt"
    cat(paste0("\n- processing ",TRAIT,"\n\n"))
    currentDF <- as.data.frame(AERNASE.clin.hdac9 %>%
      dplyr::select(., TARGET, TRAIT, COVARIATES_M2) %>%
      filter(complete.cases(.))) %>%
      filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
    # for debug
    # print(DT::datatable(currentDF))
    # print(nrow(currentDF))
    # print(str(currentDF))
    # print(class(currentDF[,TRAIT]))
    ### univariate

    # fit <- glm(as.factor(currentDF[,TRAIT]) ~ currentDF[,TARGET] + Age + Gender + ORdate_year + 
    #              Hypertension.composite + DiabetesStatus + SmokerStatus + 
    #              Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
    #              MedHx_CVD + stenose, 
    #            data  =  currentDF, family = binomial(link = "logit"))
    
    fit <- glm(as.factor(currentDF[,TRAIT]) ~ currentDF[,TARGET] + Age + Gender + ORdate_epoch + 
                 Hypertension.composite + DiabetesStatus + SmokerStatus + 
                 Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
                 MedHx_CVD + stenose, 
               data  =  currentDF, family = binomial(link = "logit"))
    
    model_step <- stepAIC(fit, direction = "both", trace = FALSE)
    print(model_step)
    print(summary(fit))
    
    GLM.results.TEMP <- data.frame(matrix(NA, ncol = 16, nrow = 0))
    GLM.results.TEMP[1,] = GLM.BIN(fit, "AERNASE.clin.hdac9", TARGET, TRAIT, verbose = TRUE)
    GLM.results = rbind(GLM.results, GLM.results.TEMP)
  }

Analysis of HDAC9.

- processing AsymptSympt


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, TARGET] + 
    Age + BMI, family = binomial(link = "logit"), data = currentDF)

Coefficients:
        (Intercept)  currentDF[, TARGET]                  Age                  BMI  
           0.583817             0.008055             0.037885            -0.052261  

Degrees of Freedom: 536 Total (i.e. Null);  533 Residual
Null Deviance:      408 
Residual Deviance: 396.3    AIC: 404.3

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, TARGET] + 
    Age + Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, family = binomial(link = "logit"), 
    data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.6448   0.3364   0.4649   0.5673   0.9144  

Coefficients:
                            Estimate Std. Error z value Pr(>|z|)  
(Intercept)                1.467e+01  1.690e+03   0.009    0.993  
currentDF[, TARGET]        8.221e-03  6.461e-03   1.272    0.203  
Age                        4.453e-02  1.773e-02   2.512    0.012 *
Gendermale                -2.256e-01  3.327e-01  -0.678    0.498  
ORdate_epoch               7.762e-05  1.357e-04   0.572    0.567  
Hypertension.compositeyes -3.389e-01  4.693e-01  -0.722    0.470  
DiabetesStatusDiabetes     3.098e-02  3.300e-01   0.094    0.925  
SmokerStatusEx-smoker     -3.760e-01  3.154e-01  -1.192    0.233  
SmokerStatusNever smoked  -5.411e-01  3.989e-01  -1.357    0.175  
Med.Statin.LLDyes         -2.513e-01  3.538e-01  -0.710    0.477  
Med.all.antiplateletyes   -2.599e-01  5.583e-01  -0.466    0.642  
GFR_MDRD                   1.138e-03  7.188e-03   0.158    0.874  
BMI                       -4.275e-02  3.442e-02  -1.242    0.214  
MedHx_CVDNo                7.272e-02  2.819e-01   0.258    0.796  
stenose50-70%             -1.333e+01  1.690e+03  -0.008    0.994  
stenose70-90%             -1.483e+01  1.690e+03  -0.009    0.993  
stenose90-99%             -1.484e+01  1.690e+03  -0.009    0.993  
stenose100% (Occlusion)   -3.106e-01  2.066e+03   0.000    1.000  
stenose50-99%             -7.321e-01  2.935e+03   0.000    1.000  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 408.04  on 536  degrees of freedom
Residual deviance: 384.85  on 518  degrees of freedom
AIC: 422.85

Number of Fisher Scoring iterations: 15

Analyzing in dataset ' AERNASE.clin.hdac9 ' the association of ' HDAC9 ' with ' AsymptSympt ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.hdac9 
Score/Exposure/biomarker..: HDAC9 
Trait/outcome.............: AsymptSympt 
Effect size...............: 0.008221 
Standard error............: 0.006461 
Odds ratio (effect size)..: 1.008 
Lower 95% CI..............: 0.996 
Upper 95% CI..............: 1.021 
Z-value...................: 1.272457 
P-value...................: 0.203211 
Hosmer and Lemeshow r^2...: 0.056844 
Cox and Snell r^2.........: 0.042274 
Nagelkerke's pseudo r^2...: 0.079423 
Sample size of AE DB......: 623 
Sample size of model......: 537 
Missing data %............: 13.80417 
cat("Edit the column names...\n")
Edit the column names...
colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
                          "Beta", "s.e.m.",
                          "OR", "low95CI", "up95CI",
                          "Z-value", "P-value", "r^2_l", "r^2_cs", "r^2_nagelkerke", "N", "Model_N", "Perc_Miss")

cat("Correct the variable types...\n")
Correct the variable types...
GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`Z-value` <- as.numeric(GLM.results$`Z-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2_l` <- as.numeric(GLM.results$`r^2_l`)
GLM.results$`r^2_cs` <- as.numeric(GLM.results$`r^2_cs`)
GLM.results$`r^2_nagelkerke` <- as.numeric(GLM.results$`r^2_nagelkerke`)
GLM.results$`N` <- as.numeric(GLM.results$`N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)

# Save the data
cat("Writing results to Excel-file...\n")
Writing results to Excel-file...
### Univariate
write.xlsx(GLM.results,
           file = paste0(OUT_loc, "/",Today,".AERNASE.clin.hdac9.Bin.Multi.",TRAIT_OF_INTEREST,".RANK.Symptoms.MODEL2.xlsx"),
           rowNames = FALSE, colNmes = TRUE, sheetName = "Bin.Multi.Symptoms")

# Removing intermediates
cat("Removing intermediate files...\n")
Removing intermediate files...
rm(TRAIT, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)

C. Longitudinal analysis secondary clinical outcome

For the longitudinal analyses of plaque HDAC9 expression levels and secondary cardiovascular events over a three-year follow-up period.

The primary outcome is defined as “a composite of fatal or non-fatal myocardial infarction, fatal or non-fatal stroke, ruptured aortic aneurysm, fatal cardiac failure, coronary or peripheral interventions, leg amputation due to vascular causes, and cardiovascular death”, i.e. major adverse cardiovascular events (MACE). Variable: epmajor.3years, these include: - myocardial infarction (MI) - cerebral infarction (CVA/stroke) - cardiovascular death (exact cause to be investigated) - cerebral bleeding (CVA/stroke) - fatal myocardial infarction (MI) - fatal cerebral infarction - fatal cerebral bleeding - sudden death - fatal heart failure - fatal aneurysm rupture - other cardiovascular death..

The secondary outcomes will be

  • incidence of fatal or non-fatal stroke (ischemic and bleeding) - variable: epstroke.3years, these include:
    • cerebral infarction (CVA/stroke)
    • cerebral bleeding (CVA/stroke)
    • fatal cerebral infarction
    • fatal cerebral bleeding.
  • incidence of acute coronary events (fatal or non-fatal myocardial infarction, coronary interventions) - variable: epcoronary.3years, these include:
    • myocardial infarction (MI)
    • coronary angioplasty (PCI/PTCA)
    • cardiovascular death (exact cause to be investigated)
    • coronary bypass (CABG)
    • fatal myocardial infarction (MI)
    • sudden death.
  • cardiovascular death - variable: epcvdeath.3years, these include:
    • cardiovascular death (exact cause to be investigated)
    • fatal myocardial infarction (MI)
    • fatal cerebral infarction
    • fatal cerebral bleeding
    • sudden death
    • fatal heart failure
    • fatal aneurysm rupture
    • other cardiovascular death..

30- and 90-days FU events

We will use 3-year follow-up, but we will also calculate 30 days and 90 days follow-up ‘time-to-event’ variables. On average there are 365.25 days in a year. We can calculate 30-days and 90-days follow-up time based on the three years follow-up.

cutt.off.30days = (1/365.25) * 30
cutt.off.90days = (1/365.25) * 90

# Fix maximum FU of 30 and 90 days
AEDB.CEA <- AEDB.CEA %>%
  mutate(
    FU.cutt.off.30days = ifelse(max.followup <= cutt.off.30days, max.followup, cutt.off.30days),
    FU.cutt.off.90days = ifelse(max.followup <= cutt.off.90days, max.followup, cutt.off.90days)
  ) 

AEDB.temp <- subset(AEDB.CEA,  select = c("STUDY_NUMBER", "Age", "Gender", "Hospital", "Artery_summary", 
                                      "max.followup", 
                                      "FU.cutt.off.3years",
                                      "FU.cutt.off.30days", 
                                      "FU.cutt.off.90days"))
require(labelled)
AEDB.temp$Gender <- to_factor(AEDB.temp$Gender)
AEDB.temp$Hospital <- to_factor(AEDB.temp$Hospital)
AEDB.temp$Artery_summary <- to_factor(AEDB.temp$Artery_summary)

DT::datatable(AEDB.temp[1:10,], caption = "Excerpt of the whole AEDB.", rownames = FALSE)

rm(AEDB.temp)

AERNASE.clin.hdac9 <- merge(AERNASE.clin.hdac9, 
                            subset(AEDB.CEA, select = c("STUDY_NUMBER", "max.followup", 
                                                        "FU.cutt.off.3years", "FU.cutt.off.30days", "FU.cutt.off.90days",
                                                        "ep_major_t_3years", "ep_stroke_t_3years", "ep_coronary_t_3years",
                                                        "ep_cvdeath_t_3years",
                                                        "epmajor.3years", "epstroke.3years", "epcoronary.3years", "epcvdeath.3years"
                                                        )), 
                            by.x = "STUDY_NUMBER", by.y = "STUDY_NUMBER", sort = TRUE)


AERNASE.clin.hdac9.temp <- subset(AERNASE.clin.hdac9,  select = c("STUDY_NUMBER", "Age", "Gender", "Hospital", "Artery_summary", 
                                      "max.followup", 
                                      "FU.cutt.off.3years",
                                      "FU.cutt.off.30days", 
                                      "FU.cutt.off.90days"))
require(labelled)
AERNASE.clin.hdac9.temp$Gender <- to_factor(AERNASE.clin.hdac9.temp$Gender)
AERNASE.clin.hdac9.temp$Hospital <- to_factor(AERNASE.clin.hdac9.temp$Hospital)
AERNASE.clin.hdac9.temp$Artery_summary <- to_factor(AERNASE.clin.hdac9.temp$Artery_summary)

DT::datatable(AERNASE.clin.hdac9.temp[1:10,], caption = "Excerpt of the whole AERNASE.clin.hdac9.", rownames = FALSE)

rm(AERNASE.clin.hdac9.temp)

Here we will calculate the new 30- and 90-days follow-up of the events and their event-times of interest:

  • MACE (epmajor.3years)
  • Stroke (epstroke.3years)
  • Coronary events (epcoronary.3years)
  • Cardiovascular death (epcvdeath.3years)
avg_days_in_year = 365.25
cutt.off.30days.scaled <- cutt.off.30days * 365.25
cutt.off.90days.scaled <- cutt.off.90days * 365.25
# Event times

AERNASE.clin.hdac9 <- AERNASE.clin.hdac9 %>%
  mutate(
    ep_major_t_30days = ifelse(ep_major_t_3years * avg_days_in_year <= cutt.off.30days.scaled, 
                               ep_major_t_3years * avg_days_in_year, cutt.off.30days.scaled),
    ep_stroke_t_30days = ifelse(ep_stroke_t_3years * avg_days_in_year <= cutt.off.30days.scaled, 
                                ep_stroke_t_3years * avg_days_in_year, cutt.off.30days.scaled),
    ep_coronary_t_30days = ifelse(ep_coronary_t_3years * avg_days_in_year <= cutt.off.30days.scaled, 
                                  ep_coronary_t_3years * avg_days_in_year, cutt.off.30days.scaled),
    ep_cvdeath_t_30days = ifelse(ep_cvdeath_t_3years * avg_days_in_year <= cutt.off.30days.scaled, 
                                 ep_cvdeath_t_3years * avg_days_in_year, cutt.off.30days.scaled),
    ep_major_t_90days = ifelse(ep_major_t_3years * avg_days_in_year <= cutt.off.90days.scaled, 
                               ep_major_t_3years * avg_days_in_year, cutt.off.90days.scaled),
    ep_stroke_t_90days = ifelse(ep_stroke_t_3years * avg_days_in_year <= cutt.off.90days.scaled, 
                                ep_stroke_t_3years * avg_days_in_year, cutt.off.90days.scaled),
    ep_coronary_t_90days = ifelse(ep_coronary_t_3years * avg_days_in_year <= cutt.off.90days.scaled, 
                                  ep_coronary_t_3years * avg_days_in_year, cutt.off.90days.scaled),
    ep_cvdeath_t_90days = ifelse(ep_cvdeath_t_3years * avg_days_in_year <= cutt.off.90days.scaled, 
                                 ep_cvdeath_t_3years * avg_days_in_year, cutt.off.90days.scaled)
  ) 

attach(AERNASE.clin.hdac9)
AERNASE.clin.hdac9[,"epmajor.30days"] <- AERNASE.clin.hdac9$epmajor.3years
AERNASE.clin.hdac9$epmajor.30days[epmajor.3years == 1 & ep_major_t_3years > cutt.off.30days] <- 0

AERNASE.clin.hdac9[,"epstroke.30days"] <- AERNASE.clin.hdac9$epstroke.3years
AERNASE.clin.hdac9$epstroke.30days[epstroke.3years == 1 & ep_stroke_t_3years > cutt.off.30days] <- 0

AERNASE.clin.hdac9[,"epcoronary.30days"] <- AERNASE.clin.hdac9$epcoronary.3years
AERNASE.clin.hdac9$epcoronary.30days[epcoronary.3years == 1 & ep_coronary_t_3years > cutt.off.30days] <- 0

AERNASE.clin.hdac9[,"epcvdeath.30days"] <- AERNASE.clin.hdac9$epcvdeath.3years
AERNASE.clin.hdac9$epcvdeath.30days[epcvdeath.3years == 1 & ep_cvdeath_t_3years > cutt.off.30days] <- 0

AERNASE.clin.hdac9[,"epmajor.90days"] <- AERNASE.clin.hdac9$epmajor.3years
AERNASE.clin.hdac9$epmajor.90days[epmajor.3years == 1 & ep_major_t_3years > cutt.off.90days] <- 0

AERNASE.clin.hdac9[,"epstroke.90days"] <- AERNASE.clin.hdac9$epstroke.3years
AERNASE.clin.hdac9$epstroke.90days[epstroke.3years == 1 & ep_stroke_t_3years > cutt.off.90days] <- 0

AERNASE.clin.hdac9[,"epcoronary.90days"] <- AERNASE.clin.hdac9$epcoronary.3years
AERNASE.clin.hdac9$epcoronary.90days[epcoronary.3years == 1 & ep_coronary_t_3years > cutt.off.90days] <- 0

AERNASE.clin.hdac9[,"epcvdeath.90days"] <- AERNASE.clin.hdac9$epcvdeath.3years
AERNASE.clin.hdac9$epcvdeath.90days[epcvdeath.3years == 1 & ep_cvdeath_t_3years > cutt.off.90days] <- 0

detach(AERNASE.clin.hdac9)

AERNASE.clin.hdac9.temp <- subset(AERNASE.clin.hdac9,  select = c("STUDY_NUMBER", "Age", "Gender", "Hospital", "Artery_summary", 
                                      "epmajor.3years", "epstroke.3years", "epcoronary.3years", "epcvdeath.3years",
                                      "epmajor.30days", "epstroke.30days", "epcoronary.30days", "epcvdeath.30days",
                                      "epmajor.90days", "epstroke.90days", "epcoronary.90days", "epcvdeath.90days"))
require(labelled)
AERNASE.clin.hdac9.temp$Gender <- to_factor(AERNASE.clin.hdac9.temp$Gender)
AERNASE.clin.hdac9.temp$Hospital <- to_factor(AERNASE.clin.hdac9.temp$Hospital)
AERNASE.clin.hdac9.temp$Artery_summary <- to_factor(AERNASE.clin.hdac9.temp$Artery_summary)

DT::datatable(AERNASE.clin.hdac9.temp[1:10,], caption = "Excerpt of the whole AERNASE.clin.hdac9.", rownames = FALSE)

rm(AERNASE.clin.hdac9.temp)

Sanity checks

First we do some sanity checks and inventory the time-to-event and event variables.

# Reference: https://bioconductor.org/packages/devel/bioc/vignettes/MultiAssayExperiment/inst/doc/QuickStartMultiAssay.html
# If you want to suppress warnings and messages when installing/loading packages
# suppressPackageStartupMessages({})
install.packages.auto("survival")
install.packages.auto("survminer")
install.packages.auto("Hmisc")

cat("* Creating function to summarize Cox regression and prepare container for results.")
* Creating function to summarize Cox regression and prepare container for results.
# Function to get summary statistics from Cox regression model
COX.STAT <- function(coxfit, DATASET, OUTCOME, target_of_interest){
  cat("Summarizing Cox regression results for '", target_of_interest ,"' and its association to '",OUTCOME,"' in '",DATASET,"'.\n")
  if (nrow(summary(coxfit)$coefficients) == 1) {
    output = c(target_of_interest, rep(NA,8))
    cat("Model not fitted; probably singular.\n")
  }else {
    cat("Collecting data.\n\n")
    cox.sum <- summary(coxfit)
    cox.effectsize = cox.sum$coefficients[1,1]
    cox.SE = cox.sum$coefficients[1,3]
    cox.HReffect = cox.sum$coefficients[1,2]
    cox.CI_low = exp(cox.effectsize - 1.96 * cox.SE)
    cox.CI_up = exp(cox.effectsize + 1.96 * cox.SE)
    cox.zvalue = cox.sum$coefficients[1,4]
    cox.pvalue = cox.sum$coefficients[1,5]
    cox.sample_size = cox.sum$n
    cox.nevents = cox.sum$nevent
    
    output = c(DATASET, OUTCOME, target_of_interest, cox.effectsize, cox.SE, cox.HReffect, cox.CI_low, cox.CI_up, cox.zvalue, cox.pvalue, cox.sample_size, cox.nevents)
    cat("We have collected the following:\n")
    cat("Dataset used..............:", DATASET, "\n")
    cat("Outcome analyzed..........:", OUTCOME, "\n")
    cat("Protein...................:", target_of_interest, "\n")
    cat("Effect size...............:", round(cox.effectsize, 6), "\n")
    cat("Standard error............:", round(cox.SE, 6), "\n")
    cat("Odds ratio (effect size)..:", round(cox.HReffect, 3), "\n")
    cat("Lower 95% CI..............:", round(cox.CI_low, 3), "\n")
    cat("Upper 95% CI..............:", round(cox.CI_up, 3), "\n")
    cat("T-value...................:", round(cox.zvalue, 6), "\n")
    cat("P-value...................:", signif(cox.pvalue, 8), "\n")
    cat("Sample size in model......:", cox.sample_size, "\n")
    cat("Number of events..........:", cox.nevents, "\n")
  }
  return(output)
  print(output)
} 

times = c("ep_major_t_3years", 
          "ep_stroke_t_3years", "ep_coronary_t_3years", "ep_cvdeath_t_3years")

endpoints = c("epmajor.3years", 
              "epstroke.3years", "epcoronary.3years", "epcvdeath.3years")

cat("* Check the cases per event type - for sanity.")
* Check the cases per event type - for sanity.
for (events in endpoints){
  require(labelled)
  print(paste0("Printing the summary of: ",events))
  # print(summary(AERNASE.clin.hdac9[,events]))
  print(table(AERNASE.clin.hdac9[,events]))
}
[1] "Printing the summary of: epmajor.3years"

  0   1 
540  80 
[1] "Printing the summary of: epstroke.3years"

  0   1 
573  47 
[1] "Printing the summary of: epcoronary.3years"

  0   1 
572  48 
[1] "Printing the summary of: epcvdeath.3years"

  0   1 
592  28 
cat("* Check distribution of events over time - for sanity.")
* Check distribution of events over time - for sanity.
for (eventtimes in times){
  print(paste0("Printing the summary of: ",eventtimes))
  print(summary(AERNASE.clin.hdac9[,eventtimes]))
}
[1] "Printing the summary of: ep_major_t_3years"
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
  0.000   2.914   3.000   2.665   3.000   3.000       3 
[1] "Printing the summary of: ep_stroke_t_3years"
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
  0.000   2.929   3.000   2.708   3.000   3.000       3 
[1] "Printing the summary of: ep_coronary_t_3years"
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
  0.000   2.923   3.000   2.738   3.000   3.000       3 
[1] "Printing the summary of: ep_cvdeath_t_3years"
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
 0.0137  2.9500  3.0000  2.8246  3.0000  3.0000       3 
for (eventtime in times){
  
  print(paste0("Printing the distribution of: ",eventtime))
  p <- gghistogram(AERNASE.clin.hdac9, x = eventtime, y = "..count..",
              main = eventtime, bins = 15, 
              xlab = "year", color = uithof_color[16], fill = uithof_color[16], ggtheme = theme_minimal()) 
 print(p)
 ggsave(file = paste0(QC_loc, "/",Today,".AERNASE.clin.hdac9.EventDistributionPerYear.",eventtime,".pdf"), plot = last_plot())
}
[1] "Printing the distribution of: ep_major_t_3years"
Warning: Removed 3 rows containing non-finite values (stat_bin).
Saving 18 x 12 in image
Warning: Removed 3 rows containing non-finite values (stat_bin).
[1] "Printing the distribution of: ep_stroke_t_3years"
Warning: Removed 3 rows containing non-finite values (stat_bin).
Saving 18 x 12 in image
Warning: Removed 3 rows containing non-finite values (stat_bin).
[1] "Printing the distribution of: ep_coronary_t_3years"
Warning: Removed 3 rows containing non-finite values (stat_bin).
Saving 18 x 12 in image
Warning: Removed 3 rows containing non-finite values (stat_bin).
[1] "Printing the distribution of: ep_cvdeath_t_3years"
Warning: Removed 3 rows containing non-finite values (stat_bin).
Saving 18 x 12 in image
Warning: Removed 3 rows containing non-finite values (stat_bin).

times30 = c("ep_major_t_30days", 
          "ep_stroke_t_30days", "ep_coronary_t_30days", "ep_cvdeath_t_30days")

endpoints30 = c("epmajor.30days", 
              "epstroke.30days", "epcoronary.30days", "epcvdeath.30days")

cat("* Check the cases per event type - for sanity.")
* Check the cases per event type - for sanity.
for (events in endpoints30){
  print(paste0("Printing the summary of: ",events))
  # print(summary(AERNASE.clin.hdac9[,events]))
  print(table(AERNASE.clin.hdac9[,events]))
}
[1] "Printing the summary of: epmajor.30days"

  0   1 
599  21 
[1] "Printing the summary of: epstroke.30days"

  0   1 
602  18 
[1] "Printing the summary of: epcoronary.30days"

  0   1 
614   6 
[1] "Printing the summary of: epcvdeath.30days"

  0   1 
618   2 
cat("* Check distribution of events over time - for sanity.")
* Check distribution of events over time - for sanity.
for (eventtimes in times30){
  print(paste0("Printing the summary of: ",eventtimes))
  print(summary(AERNASE.clin.hdac9[,eventtimes]))
}
[1] "Printing the summary of: ep_major_t_30days"
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
   0.00   30.00   30.00   29.05   30.00   30.00       3 
[1] "Printing the summary of: ep_stroke_t_30days"
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
   0.00   30.00   30.00   29.19   30.00   30.00       3 
[1] "Printing the summary of: ep_coronary_t_30days"
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
   0.00   30.00   30.00   29.67   30.00   30.00       3 
[1] "Printing the summary of: ep_cvdeath_t_30days"
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
  5.003  30.000  30.000  29.937  30.000  30.000       3 
for (eventtime in times30){
  
  print(paste0("Printing the distribution of: ",eventtime))
  p <- gghistogram(AERNASE.clin.hdac9, x = eventtime, y = "..count..",
              main = eventtime, bins = 15, 
              xlab = "days", color = uithof_color[16], fill = uithof_color[16], ggtheme = theme_minimal()) 
 print(p)
 ggsave(file = paste0(QC_loc, "/",Today,".AERNASE.clin.hdac9.EventDistributionPer30Days.",eventtime,".pdf"), plot = last_plot())
}
[1] "Printing the distribution of: ep_major_t_30days"
Warning: Removed 3 rows containing non-finite values (stat_bin).
Saving 18 x 12 in image
Warning: Removed 3 rows containing non-finite values (stat_bin).
[1] "Printing the distribution of: ep_stroke_t_30days"
Warning: Removed 3 rows containing non-finite values (stat_bin).
Saving 18 x 12 in image
Warning: Removed 3 rows containing non-finite values (stat_bin).
[1] "Printing the distribution of: ep_coronary_t_30days"
Warning: Removed 3 rows containing non-finite values (stat_bin).
Saving 18 x 12 in image
Warning: Removed 3 rows containing non-finite values (stat_bin).
[1] "Printing the distribution of: ep_cvdeath_t_30days"
Warning: Removed 3 rows containing non-finite values (stat_bin).
Saving 18 x 12 in image
Warning: Removed 3 rows containing non-finite values (stat_bin).

times90 = c("ep_major_t_90days", 
          "ep_stroke_t_90days", "ep_coronary_t_90days", "ep_cvdeath_t_90days")

endpoints90 = c("epmajor.90days", 
              "epstroke.90days", "epcoronary.90days", "epcvdeath.90days")

cat("* Check the cases per event type - for sanity.")
* Check the cases per event type - for sanity.
for (events in endpoints90){
  print(paste0("Printing the summary of: ",events))
  # print(summary(AERNASE.clin.hdac9[,events]))
  print(table(AERNASE.clin.hdac9[,events]))
}
[1] "Printing the summary of: epmajor.90days"

  0   1 
596  24 
[1] "Printing the summary of: epstroke.90days"

  0   1 
600  20 
[1] "Printing the summary of: epcoronary.90days"

  0   1 
612   8 
[1] "Printing the summary of: epcvdeath.90days"

  0   1 
617   3 
cat("* Check distribution of events over time - for sanity.")
* Check distribution of events over time - for sanity.
for (eventtimes in times90){
  print(paste0("Printing the summary of: ",eventtimes))
  print(summary(AERNASE.clin.hdac9[,eventtimes]))
}
[1] "Printing the summary of: ep_major_t_90days"
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
   0.00   90.00   90.00   86.79   90.00   90.00       3 
[1] "Printing the summary of: ep_stroke_t_90days"
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
   0.00   90.00   90.00   87.25   90.00   90.00       3 
[1] "Printing the summary of: ep_coronary_t_90days"
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
   0.00   90.00   90.00   88.75   90.00   90.00       3 
[1] "Printing the summary of: ep_cvdeath_t_90days"
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
  5.003  90.000  90.000  89.674  90.000  90.000       3 
for (eventtime in times90){
  
  print(paste0("Printing the distribution of: ",eventtime))
  p <- gghistogram(AERNASE.clin.hdac9, x = eventtime, y = "..count..",
              main = eventtime, bins = 15, 
              xlab = "days", color = uithof_color[16], fill = uithof_color[16], ggtheme = theme_minimal()) 
 print(p)
 ggsave(file = paste0(QC_loc, "/",Today,".AERNASE.clin.hdac9.EventDistributionPer90Days.",eventtime,".pdf"), plot = last_plot())
}
[1] "Printing the distribution of: ep_major_t_90days"
Warning: Removed 3 rows containing non-finite values (stat_bin).
Saving 18 x 12 in image
Warning: Removed 3 rows containing non-finite values (stat_bin).
[1] "Printing the distribution of: ep_stroke_t_90days"
Warning: Removed 3 rows containing non-finite values (stat_bin).
Saving 18 x 12 in image
Warning: Removed 3 rows containing non-finite values (stat_bin).
[1] "Printing the distribution of: ep_coronary_t_90days"
Warning: Removed 3 rows containing non-finite values (stat_bin).
Saving 18 x 12 in image
Warning: Removed 3 rows containing non-finite values (stat_bin).
[1] "Printing the distribution of: ep_cvdeath_t_90days"
Warning: Removed 3 rows containing non-finite values (stat_bin).
Saving 18 x 12 in image
Warning: Removed 3 rows containing non-finite values (stat_bin).

Cox regressions

Let’s perform the actual Cox-regressions. We will apply a couple of models:

  • Model 1: adjusted for age, sex, and year of surgery
  • Model 2: adjusted for age, sex, year of surgery, hypertension, diabetes, smoking, LDL-C levels, lipid-lowering drugs, antiplatelet drugs, eGFR, BMI, history of CVD, level of stenosis

3 years follow-up

Model 1
# Set up a dataframe to receive results
COX.results <- data.frame(matrix(NA, ncol = 12, nrow = 0))

# Looping over each target_of_interest/endpoint/time combination
for (i in 1:length(times)){
  eptime = times[i]
  ep = endpoints[i]
  cat(paste0("* Analyzing the effect of plaque target-of-interest on [",ep,"].\n"))
  cat(" - creating temporary SE for this work.\n")
  TEMP.DF = as.data.frame(AERNASE.clin.hdac9)
  cat(" - making a 'Surv' object and adding this to temporary dataframe.\n")
  TEMP.DF$event <- as.integer(TEMP.DF[,ep])
  TEMP.DF$y <- Surv(time = TEMP.DF[,eptime], event = TEMP.DF$event)
  cat(" - making strata of each of the plaque target-of-interest and start survival analysis.\n")
  
  for (target_of_interest in 1:length(TRAITS.TARGET.RANK)){
    cat(paste0("   > processing [",TRAITS.TARGET.RANK[target_of_interest],"]; ",target_of_interest," out of ",length(TRAITS.TARGET.RANK)," target-of-interest.\n"))
    # splitting into two groups
    TEMP.DF[[ TRAITS.TARGET.RANK[target_of_interest] ]] <- cut2(TEMP.DF[,TRAITS.TARGET.RANK[target_of_interest]], g = 2)
    cat(paste0("   > cross tabulation of ",TRAITS.TARGET.RANK[target_of_interest],"-stratum.\n"))
    show(table(TEMP.DF[[ TRAITS.TARGET.RANK[target_of_interest] ]]))
    
    cat(paste0("\n   > fitting the model for ",TRAITS.TARGET.RANK[target_of_interest],"-stratum.\n"))
    fit <- survfit(as.formula(paste0("y ~ ", TRAITS.TARGET.RANK[target_of_interest])), data = TEMP.DF)
    
    cat(paste0("\n   > make a Kaplan-Meier-shizzle...\n"))
    # make Kaplan-Meier curve and save it
    show(ggsurvplot(fit, data = TEMP.DF,
                    palette = c("#DB003F", "#1290D9"),
                    # palete = c("F59D10", "#DB003F", "#49A01D", "#1290D9"),
                    linetype = c(1,2),
                    # linetype = c(1,2,3,4),
                    # conf.int = FALSE, conf.int.fill = "#595A5C", conf.int.alpha = 0.1,
                    pval = FALSE, pval.method = FALSE, pval.size = 4,
                    risk.table = TRUE, risk.table.y.text = FALSE, tables.y.text.col = TRUE, fontsize = 4,
                    censor = FALSE,
                    legend = "right",
                    legend.title = paste0("",TRAITS.TARGET.RANK[target_of_interest],""),
                    legend.labs = c("low", "high"),
                    title = paste0("Risk of ",ep,""), xlab = "Time [years]", font.main = c(16, "bold", "black")))
    dev.copy2pdf(file = paste0(COX_loc,"/",
                               Today,".AERNASE.clin.hdac9.survival.",ep,".2G.",
                               TRAITS.TARGET.RANK[target_of_interest],".pdf"), width = 12, height = 10, onefile = FALSE)

    cat(paste0("\n   > perform the Cox-regression fashizzle and plot it...\n"))
    ### Do Cox-regression and plot it
    
    ### MODEL 1 (Simple model)
    cox = coxph(Surv(TEMP.DF[,eptime], event) ~ TEMP.DF[[ TRAITS.TARGET.RANK[target_of_interest] ]]+Age+Gender + ORdate_year, data = TEMP.DF)
    coxplot = coxph(Surv(TEMP.DF[,eptime], event) ~ strata(TEMP.DF[[ TRAITS.TARGET.RANK[target_of_interest] ]])+Age+Gender + ORdate_year, data = TEMP.DF)

    plot(survfit(coxplot), main = paste0("Cox proportional hazard of [",ep,"] per [",eptime,"]."),
         # ylim = c(0.2, 1), xlim = c(0,3), col = c("#595A5C", "#DB003F", "#1290D9"),
         ylim = c(0, 1), xlim = c(0,3), col = c("#DB003F", "#1290D9"),
         lty = c(1,2), lwd = 2,
         ylab = "Suvival probability", xlab = "FU time [years]",
         mark.time = FALSE, axes = FALSE, bty = "n")
    legend("topright",
           c("low", "high"),
           title = paste0("",TRAITS.TARGET.RANK[target_of_interest],""),
           col = c("#DB003F", "#1290D9"),
           lty = c(1,2), lwd = 2,
           bty = "n")
    axis(side = 1, at = seq(0, 3, by = 1))
    axis(side = 2, at = seq(0, 1, by = 0.2))
    dev.copy2pdf(file = paste0(COX_loc,"/",
                               Today,".AERNASE.clin.hdac9.Cox.",ep,".2G.",
                               # Today,".AERNASE.clin.hdac9.Cox.",ep,".4G.",
                               TRAITS.TARGET.RANK[target_of_interest],".MODEL1.pdf"), height = 12, width = 10, onefile = TRUE)
    show(summary(cox))

    cat(paste0("\n   > writing the Cox-regression fashizzle to Excel...\n"))

    COX.results.TEMP <- data.frame(matrix(NA, ncol = 12, nrow = 0))
    COX.results.TEMP[1,] = COX.STAT(cox, "AERNASE.clin.hdac9", ep, TRAITS.TARGET.RANK[target_of_interest])
    COX.results = rbind(COX.results, COX.results.TEMP)

  }
}
* Analyzing the effect of plaque target-of-interest on [epmajor.3years].
 - creating temporary SE for this work.
 - making a 'Surv' object and adding this to temporary dataframe.
 - making strata of each of the plaque target-of-interest and start survival analysis.
   > processing [HDAC9]; 1 out of 1 target-of-interest.
   > cross tabulation of HDAC9-stratum.

[ 0, 22) [22,449] 
     319      304 

   > fitting the model for HDAC9-stratum.

   > make a Kaplan-Meier-shizzle...

   > perform the Cox-regression fashizzle and plot it...
Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights,  :
  Loglik converged before variable  4,5,6,7,8,9,10,11,12,13,14 ; coefficient may be infinite. 
Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights,  :
  Loglik converged before variable  3,4,5,6,7,8,9,10,11,12,13,16 ; coefficient may be infinite. 

Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]] + 
    Age + Gender + ORdate_year, data = TEMP.DF)

  n= 620, number of events= 80 
   (3 observations deleted due to missingness)

                                                                coef  exp(coef)   se(coef)      z Pr(>|z|)  
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][22,449] -3.351e-01  7.153e-01  2.322e-01 -1.443   0.1491  
Age                                                        3.283e-02  1.033e+00  1.377e-02  2.384   0.0171 *
Gendermale                                                 7.816e-01  2.185e+00  3.270e-01  2.390   0.0168 *
ORdate_year2002                                            1.627e+01  1.165e+07  8.569e+03  0.002   0.9985  
ORdate_year2003                                            1.513e+01  3.712e+06  8.569e+03  0.002   0.9986  
ORdate_year2004                                            1.549e+01  5.320e+06  8.569e+03  0.002   0.9986  
ORdate_year2005                                            1.582e+01  7.424e+06  8.569e+03  0.002   0.9985  
ORdate_year2006                                            1.582e+01  7.416e+06  8.569e+03  0.002   0.9985  
ORdate_year2007                                            1.497e+01  3.188e+06  8.569e+03  0.002   0.9986  
ORdate_year2008                                            1.600e+01  8.864e+06  8.569e+03  0.002   0.9985  
ORdate_year2009                                            1.533e+01  4.547e+06  8.569e+03  0.002   0.9986  
ORdate_year2010                                            1.567e+01  6.404e+06  8.569e+03  0.002   0.9985  
ORdate_year2011                                            1.475e+01  2.543e+06  8.569e+03  0.002   0.9986  
ORdate_year2012                                            1.542e+01  4.972e+06  8.569e+03  0.002   0.9986  
ORdate_year2013                                           -8.764e-01  4.163e-01  9.370e+03  0.000   0.9999  
ORdate_year2014                                           -5.470e-01  5.787e-01  9.846e+03  0.000   1.0000  
ORdate_year2015                                            1.640e-01  1.178e+00  9.788e+03  0.000   1.0000  
ORdate_year2016                                                   NA         NA  0.000e+00     NA       NA  
ORdate_year2017                                                   NA         NA  0.000e+00     NA       NA  
ORdate_year2018                                                   NA         NA  0.000e+00     NA       NA  
ORdate_year2019                                                   NA         NA  0.000e+00     NA       NA  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

                                                          exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][22,449] 7.153e-01  1.398e+00    0.4537     1.128
Age                                                       1.033e+00  9.677e-01    1.0059     1.062
Gendermale                                                2.185e+00  4.577e-01    1.1511     4.148
ORdate_year2002                                           1.165e+07  8.586e-08    0.0000       Inf
ORdate_year2003                                           3.712e+06  2.694e-07    0.0000       Inf
ORdate_year2004                                           5.320e+06  1.880e-07    0.0000       Inf
ORdate_year2005                                           7.424e+06  1.347e-07    0.0000       Inf
ORdate_year2006                                           7.416e+06  1.348e-07    0.0000       Inf
ORdate_year2007                                           3.188e+06  3.136e-07    0.0000       Inf
ORdate_year2008                                           8.864e+06  1.128e-07    0.0000       Inf
ORdate_year2009                                           4.547e+06  2.199e-07    0.0000       Inf
ORdate_year2010                                           6.404e+06  1.561e-07    0.0000       Inf
ORdate_year2011                                           2.543e+06  3.933e-07    0.0000       Inf
ORdate_year2012                                           4.972e+06  2.011e-07    0.0000       Inf
ORdate_year2013                                           4.163e-01  2.402e+00    0.0000       Inf
ORdate_year2014                                           5.787e-01  1.728e+00    0.0000       Inf
ORdate_year2015                                           1.178e+00  8.488e-01    0.0000       Inf
ORdate_year2016                                                  NA         NA        NA        NA
ORdate_year2017                                                  NA         NA        NA        NA
ORdate_year2018                                                  NA         NA        NA        NA
ORdate_year2019                                                  NA         NA        NA        NA

Concordance= 0.656  (se = 0.029 )
Likelihood ratio test= 28.81  on 17 df,   p=0.04
Wald test            = 23.66  on 17 df,   p=0.1
Score (logrank) test = 26.46  on 17 df,   p=0.07


   > writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' HDAC9 ' and its association to ' epmajor.3years ' in ' AERNASE.clin.hdac9 '.
Collecting data.

We have collected the following:
Dataset used..............: AERNASE.clin.hdac9 
Outcome analyzed..........: epmajor.3years 
Protein...................: HDAC9 
Effect size...............: -0.335091 
Standard error............: 0.232242 
Odds ratio (effect size)..: 0.715 
Lower 95% CI..............: 0.454 
Upper 95% CI..............: 1.128 
T-value...................: -1.442853 
P-value...................: 0.1490618 
Sample size in model......: 620 
Number of events..........: 80 
* Analyzing the effect of plaque target-of-interest on [epstroke.3years].
 - creating temporary SE for this work.
 - making a 'Surv' object and adding this to temporary dataframe.
 - making strata of each of the plaque target-of-interest and start survival analysis.
   > processing [HDAC9]; 1 out of 1 target-of-interest.
   > cross tabulation of HDAC9-stratum.

[ 0, 22) [22,449] 
     319      304 

   > fitting the model for HDAC9-stratum.

   > make a Kaplan-Meier-shizzle...

   > perform the Cox-regression fashizzle and plot it...
Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights,  :
  Loglik converged before variable  4,5,6,7,8,9,10,11,12,13,14 ; coefficient may be infinite. 
Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights,  :
  Loglik converged before variable  3,4,5,6,7,8,9,10,11,12,13 ; coefficient may be infinite. 

Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]] + 
    Age + Gender + ORdate_year, data = TEMP.DF)

  n= 620, number of events= 47 
   (3 observations deleted due to missingness)

                                                                coef  exp(coef)   se(coef)      z Pr(>|z|)
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][22,449] -3.870e-01  6.791e-01  3.038e-01 -1.274    0.203
Age                                                        2.669e-02  1.027e+00  1.780e-02  1.499    0.134
Gendermale                                                 3.709e-01  1.449e+00  3.741e-01  0.991    0.322
ORdate_year2002                                            1.657e+01  1.571e+07  1.150e+04  0.001    0.999
ORdate_year2003                                            1.557e+01  5.754e+06  1.150e+04  0.001    0.999
ORdate_year2004                                            1.575e+01  6.918e+06  1.150e+04  0.001    0.999
ORdate_year2005                                            1.625e+01  1.136e+07  1.150e+04  0.001    0.999
ORdate_year2006                                            1.650e+01  1.465e+07  1.150e+04  0.001    0.999
ORdate_year2007                                            1.501e+01  3.297e+06  1.150e+04  0.001    0.999
ORdate_year2008                                            1.637e+01  1.292e+07  1.150e+04  0.001    0.999
ORdate_year2009                                            1.506e+01  3.460e+06  1.150e+04  0.001    0.999
ORdate_year2010                                            1.641e+01  1.343e+07  1.150e+04  0.001    0.999
ORdate_year2011                                            1.502e+01  3.329e+06  1.150e+04  0.001    0.999
ORdate_year2012                                            1.602e+01  9.024e+06  1.150e+04  0.001    0.999
ORdate_year2013                                           -4.458e-01  6.403e-01  1.258e+04  0.000    1.000
ORdate_year2014                                           -1.032e-01  9.019e-01  1.321e+04  0.000    1.000
ORdate_year2015                                            3.256e-01  1.385e+00  1.324e+04  0.000    1.000
ORdate_year2016                                                   NA         NA  0.000e+00     NA       NA
ORdate_year2017                                                   NA         NA  0.000e+00     NA       NA
ORdate_year2018                                                   NA         NA  0.000e+00     NA       NA
ORdate_year2019                                                   NA         NA  0.000e+00     NA       NA

                                                          exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][22,449] 6.791e-01  1.473e+00    0.3744     1.232
Age                                                       1.027e+00  9.737e-01    0.9918     1.064
Gendermale                                                1.449e+00  6.901e-01    0.6960     3.017
ORdate_year2002                                           1.571e+07  6.366e-08    0.0000       Inf
ORdate_year2003                                           5.754e+06  1.738e-07    0.0000       Inf
ORdate_year2004                                           6.918e+06  1.446e-07    0.0000       Inf
ORdate_year2005                                           1.136e+07  8.801e-08    0.0000       Inf
ORdate_year2006                                           1.465e+07  6.828e-08    0.0000       Inf
ORdate_year2007                                           3.297e+06  3.033e-07    0.0000       Inf
ORdate_year2008                                           1.292e+07  7.737e-08    0.0000       Inf
ORdate_year2009                                           3.460e+06  2.891e-07    0.0000       Inf
ORdate_year2010                                           1.343e+07  7.444e-08    0.0000       Inf
ORdate_year2011                                           3.329e+06  3.004e-07    0.0000       Inf
ORdate_year2012                                           9.024e+06  1.108e-07    0.0000       Inf
ORdate_year2013                                           6.403e-01  1.562e+00    0.0000       Inf
ORdate_year2014                                           9.019e-01  1.109e+00    0.0000       Inf
ORdate_year2015                                           1.385e+00  7.221e-01    0.0000       Inf
ORdate_year2016                                                  NA         NA        NA        NA
ORdate_year2017                                                  NA         NA        NA        NA
ORdate_year2018                                                  NA         NA        NA        NA
ORdate_year2019                                                  NA         NA        NA        NA

Concordance= 0.676  (se = 0.035 )
Likelihood ratio test= 19.37  on 17 df,   p=0.3
Wald test            = 13.33  on 17 df,   p=0.7
Score (logrank) test = 17.81  on 17 df,   p=0.4


   > writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' HDAC9 ' and its association to ' epstroke.3years ' in ' AERNASE.clin.hdac9 '.
Collecting data.

We have collected the following:
Dataset used..............: AERNASE.clin.hdac9 
Outcome analyzed..........: epstroke.3years 
Protein...................: HDAC9 
Effect size...............: -0.38697 
Standard error............: 0.303845 
Odds ratio (effect size)..: 0.679 
Lower 95% CI..............: 0.374 
Upper 95% CI..............: 1.232 
T-value...................: -1.273579 
P-value...................: 0.2028128 
Sample size in model......: 620 
Number of events..........: 47 
* Analyzing the effect of plaque target-of-interest on [epcoronary.3years].
 - creating temporary SE for this work.
 - making a 'Surv' object and adding this to temporary dataframe.
 - making strata of each of the plaque target-of-interest and start survival analysis.
   > processing [HDAC9]; 1 out of 1 target-of-interest.
   > cross tabulation of HDAC9-stratum.

[ 0, 22) [22,449] 
     319      304 

   > fitting the model for HDAC9-stratum.

   > make a Kaplan-Meier-shizzle...

   > perform the Cox-regression fashizzle and plot it...
Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights,  :
  Loglik converged before variable  4,5,6,7,8,9,10,11,12,13,14 ; coefficient may be infinite. 
Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights,  :
  Loglik converged before variable  3,4,5,6,7,8,9,10,11,12,13 ; coefficient may be infinite. 

Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]] + 
    Age + Gender + ORdate_year, data = TEMP.DF)

  n= 620, number of events= 48 
   (3 observations deleted due to missingness)

                                                                coef  exp(coef)   se(coef)      z Pr(>|z|)  
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][22,449] -3.056e-01  7.367e-01  2.966e-01 -1.030   0.3029  
Age                                                        1.465e-03  1.001e+00  1.721e-02  0.085   0.9321  
Gendermale                                                 9.151e-01  2.497e+00  4.391e-01  2.084   0.0372 *
ORdate_year2002                                            1.639e+01  1.313e+07  1.115e+04  0.001   0.9988  
ORdate_year2003                                            1.578e+01  7.114e+06  1.115e+04  0.001   0.9989  
ORdate_year2004                                            1.496e+01  3.140e+06  1.115e+04  0.001   0.9989  
ORdate_year2005                                            1.571e+01  6.629e+06  1.115e+04  0.001   0.9989  
ORdate_year2006                                            1.601e+01  8.936e+06  1.115e+04  0.001   0.9989  
ORdate_year2007                                            1.553e+01  5.546e+06  1.115e+04  0.001   0.9989  
ORdate_year2008                                            1.562e+01  6.058e+06  1.115e+04  0.001   0.9989  
ORdate_year2009                                            1.533e+01  4.545e+06  1.115e+04  0.001   0.9989  
ORdate_year2010                                            1.454e+01  2.063e+06  1.115e+04  0.001   0.9990  
ORdate_year2011                                            1.464e+01  2.285e+06  1.115e+04  0.001   0.9990  
ORdate_year2012                                            1.484e+01  2.782e+06  1.115e+04  0.001   0.9989  
ORdate_year2013                                           -9.184e-01  3.991e-01  1.222e+04  0.000   0.9999  
ORdate_year2014                                           -7.102e-01  4.915e-01  1.285e+04  0.000   1.0000  
ORdate_year2015                                           -1.174e-01  8.892e-01  1.280e+04  0.000   1.0000  
ORdate_year2016                                                   NA         NA  0.000e+00     NA       NA  
ORdate_year2017                                                   NA         NA  0.000e+00     NA       NA  
ORdate_year2018                                                   NA         NA  0.000e+00     NA       NA  
ORdate_year2019                                                   NA         NA  0.000e+00     NA       NA  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

                                                          exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][22,449] 7.367e-01  1.357e+00    0.4119     1.318
Age                                                       1.001e+00  9.985e-01    0.9682     1.036
Gendermale                                                2.497e+00  4.005e-01    1.0560     5.905
ORdate_year2002                                           1.313e+07  7.614e-08    0.0000       Inf
ORdate_year2003                                           7.114e+06  1.406e-07    0.0000       Inf
ORdate_year2004                                           3.140e+06  3.185e-07    0.0000       Inf
ORdate_year2005                                           6.629e+06  1.508e-07    0.0000       Inf
ORdate_year2006                                           8.936e+06  1.119e-07    0.0000       Inf
ORdate_year2007                                           5.546e+06  1.803e-07    0.0000       Inf
ORdate_year2008                                           6.058e+06  1.651e-07    0.0000       Inf
ORdate_year2009                                           4.545e+06  2.200e-07    0.0000       Inf
ORdate_year2010                                           2.063e+06  4.848e-07    0.0000       Inf
ORdate_year2011                                           2.285e+06  4.376e-07    0.0000       Inf
ORdate_year2012                                           2.782e+06  3.595e-07    0.0000       Inf
ORdate_year2013                                           3.991e-01  2.505e+00    0.0000       Inf
ORdate_year2014                                           4.915e-01  2.034e+00    0.0000       Inf
ORdate_year2015                                           8.892e-01  1.125e+00    0.0000       Inf
ORdate_year2016                                                  NA         NA        NA        NA
ORdate_year2017                                                  NA         NA        NA        NA
ORdate_year2018                                                  NA         NA        NA        NA
ORdate_year2019                                                  NA         NA        NA        NA

Concordance= 0.654  (se = 0.038 )
Likelihood ratio test= 17.21  on 17 df,   p=0.4
Wald test            = 13.48  on 17 df,   p=0.7
Score (logrank) test = 15.71  on 17 df,   p=0.5


   > writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' HDAC9 ' and its association to ' epcoronary.3years ' in ' AERNASE.clin.hdac9 '.
Collecting data.

We have collected the following:
Dataset used..............: AERNASE.clin.hdac9 
Outcome analyzed..........: epcoronary.3years 
Protein...................: HDAC9 
Effect size...............: -0.305628 
Standard error............: 0.296646 
Odds ratio (effect size)..: 0.737 
Lower 95% CI..............: 0.412 
Upper 95% CI..............: 1.318 
T-value...................: -1.030276 
P-value...................: 0.3028806 
Sample size in model......: 620 
Number of events..........: 48 
* Analyzing the effect of plaque target-of-interest on [epcvdeath.3years].
 - creating temporary SE for this work.
 - making a 'Surv' object and adding this to temporary dataframe.
 - making strata of each of the plaque target-of-interest and start survival analysis.
   > processing [HDAC9]; 1 out of 1 target-of-interest.
   > cross tabulation of HDAC9-stratum.

[ 0, 22) [22,449] 
     319      304 

   > fitting the model for HDAC9-stratum.

   > make a Kaplan-Meier-shizzle...

   > perform the Cox-regression fashizzle and plot it...
Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights,  :
  Loglik converged before variable  4,5,6,7,8,9,10,11,12 ; coefficient may be infinite. 
Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights,  :
  Loglik converged before variable  3,4,5,6,7,8,9,10,11 ; coefficient may be infinite. 

Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]] + 
    Age + Gender + ORdate_year, data = TEMP.DF)

  n= 620, number of events= 28 
   (3 observations deleted due to missingness)

                                                                coef  exp(coef)   se(coef)      z Pr(>|z|)   
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][22,449] -7.408e-01  4.768e-01  4.175e-01 -1.774  0.07603 . 
Age                                                        7.826e-02  1.081e+00  2.610e-02  2.999  0.00271 **
Gendermale                                                 6.706e-01  1.955e+00  5.434e-01  1.234  0.21717   
ORdate_year2002                                            1.857e+01  1.158e+08  3.447e+04  0.001  0.99957   
ORdate_year2003                                            1.695e+01  2.296e+07  3.447e+04  0.000  0.99961   
ORdate_year2004                                            1.684e+01  2.054e+07  3.447e+04  0.000  0.99961   
ORdate_year2005                                            1.756e+01  4.239e+07  3.447e+04  0.001  0.99959   
ORdate_year2006                                            1.665e+01  1.701e+07  3.447e+04  0.000  0.99961   
ORdate_year2007                                            1.720e+01  2.962e+07  3.447e+04  0.000  0.99960   
ORdate_year2008                                            1.730e+01  3.257e+07  3.447e+04  0.001  0.99960   
ORdate_year2009                                            1.758e+01  4.302e+07  3.447e+04  0.001  0.99959   
ORdate_year2010                                            1.749e+01  3.927e+07  3.447e+04  0.001  0.99960   
ORdate_year2011                                           -5.884e-01  5.552e-01  3.487e+04  0.000  0.99999   
ORdate_year2012                                           -6.736e-01  5.099e-01  3.507e+04  0.000  0.99998   
ORdate_year2013                                           -9.400e-01  3.906e-01  3.728e+04  0.000  0.99998   
ORdate_year2014                                           -1.580e-01  8.539e-01  3.894e+04  0.000  1.00000   
ORdate_year2015                                            7.661e-01  2.151e+00  3.907e+04  0.000  0.99998   
ORdate_year2016                                                   NA         NA  0.000e+00     NA       NA   
ORdate_year2017                                                   NA         NA  0.000e+00     NA       NA   
ORdate_year2018                                                   NA         NA  0.000e+00     NA       NA   
ORdate_year2019                                                   NA         NA  0.000e+00     NA       NA   
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

                                                          exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][22,449] 4.768e-01  2.098e+00    0.2103     1.081
Age                                                       1.081e+00  9.247e-01    1.0275     1.138
Gendermale                                                1.955e+00  5.114e-01    0.6740     5.673
ORdate_year2002                                           1.158e+08  8.639e-09    0.0000       Inf
ORdate_year2003                                           2.296e+07  4.356e-08    0.0000       Inf
ORdate_year2004                                           2.054e+07  4.868e-08    0.0000       Inf
ORdate_year2005                                           4.239e+07  2.359e-08    0.0000       Inf
ORdate_year2006                                           1.701e+07  5.880e-08    0.0000       Inf
ORdate_year2007                                           2.962e+07  3.376e-08    0.0000       Inf
ORdate_year2008                                           3.257e+07  3.070e-08    0.0000       Inf
ORdate_year2009                                           4.302e+07  2.325e-08    0.0000       Inf
ORdate_year2010                                           3.927e+07  2.546e-08    0.0000       Inf
ORdate_year2011                                           5.552e-01  1.801e+00    0.0000       Inf
ORdate_year2012                                           5.099e-01  1.961e+00    0.0000       Inf
ORdate_year2013                                           3.906e-01  2.560e+00    0.0000       Inf
ORdate_year2014                                           8.539e-01  1.171e+00    0.0000       Inf
ORdate_year2015                                           2.151e+00  4.648e-01    0.0000       Inf
ORdate_year2016                                                  NA         NA        NA        NA
ORdate_year2017                                                  NA         NA        NA        NA
ORdate_year2018                                                  NA         NA        NA        NA
ORdate_year2019                                                  NA         NA        NA        NA

Concordance= 0.792  (se = 0.037 )
Likelihood ratio test= 30.55  on 17 df,   p=0.02
Wald test            = 14.69  on 17 df,   p=0.6
Score (logrank) test = 30.08  on 17 df,   p=0.03


   > writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' HDAC9 ' and its association to ' epcvdeath.3years ' in ' AERNASE.clin.hdac9 '.
Collecting data.

We have collected the following:
Dataset used..............: AERNASE.clin.hdac9 
Outcome analyzed..........: epcvdeath.3years 
Protein...................: HDAC9 
Effect size...............: -0.740749 
Standard error............: 0.417516 
Odds ratio (effect size)..: 0.477 
Lower 95% CI..............: 0.21 
Upper 95% CI..............: 1.081 
T-value...................: -1.77418 
P-value...................: 0.07603341 
Sample size in model......: 620 
Number of events..........: 28 

cat("- Edit the column names...\n")
- Edit the column names...
colnames(COX.results) = c("Dataset", "Outcome", "CpG",
                          "Beta", "s.e.m.",
                          "HR", "low95CI", "up95CI",
                          "Z-value", "P-value", "SampleSize", "N_events")

cat("- Correct the variable types...\n")
- Correct the variable types...
COX.results$Beta <- as.numeric(COX.results$Beta)
COX.results$s.e.m. <- as.numeric(COX.results$s.e.m.)
COX.results$HR <- as.numeric(COX.results$HR)
COX.results$low95CI <- as.numeric(COX.results$low95CI)
COX.results$up95CI <- as.numeric(COX.results$up95CI)
COX.results$`Z-value` <- as.numeric(COX.results$`Z-value`)
COX.results$`P-value` <- as.numeric(COX.results$`P-value`)
COX.results$SampleSize <- as.numeric(COX.results$SampleSize)
COX.results$N_events <- as.numeric(COX.results$N_events)

AERNASE.clin.hdac9.COX.results <- COX.results

# Save the data
cat("- Writing results to Excel-file...\n")
- Writing results to Excel-file...
head.style <- createStyle(textDecoration = "BOLD")
write.xlsx(AERNASE.clin.hdac9.COX.results,
           file = paste0(OUT_loc, "/",Today,".AERNASE.clin.hdac9.Cox.2G.MODEL1.xlsx"),
           creator = "Sander W. van der Laan",
           sheetName = "Results", headerStyle = head.style,
           rowNames = FALSE, colNames = TRUE, overwrite = TRUE)

# Removing intermediates
cat("- Removing intermediate files...\n")
- Removing intermediate files...
rm(TEMP.DF, target_of_interest, fit, cox, coxplot, COX.results, COX.results.TEMP, head.style, AERNASE.clin.hdac9.COX.results)
Model 2
# Set up a dataframe to receive results
COX.results <- data.frame(matrix(NA, ncol = 12, nrow = 0))

# Looping over each target_of_interest/endpoint/time combination
for (i in 1:length(times)){
  eptime = times[i]
  ep = endpoints[i]
  cat(paste0("* Analyzing the effect of plaque target-of-interest on [",ep,"].\n"))
  cat(" - creating temporary SE for this work.\n")
  TEMP.DF = as.data.frame(AERNASE.clin.hdac9)
  cat(" - making a 'Surv' object and adding this to temporary dataframe.\n")
  TEMP.DF$event <- as.integer(TEMP.DF[,ep])
  #as.integer(TEMP.DF[,ep] == "Excluded")

  TEMP.DF$y <- Surv(time = TEMP.DF[,eptime], event = TEMP.DF$event)
  cat(" - making strata of each of the plaque target-of-interest and start survival analysis.\n")
  
  for (target_of_interest in 1:length(TRAITS.TARGET.RANK)){
    cat(paste0("   > processing [",TRAITS.TARGET.RANK[target_of_interest],"]; ",target_of_interest," out of ",length(TRAITS.TARGET.RANK)," target-of-interest.\n"))
    # splitting into two groups
    TEMP.DF[[ TRAITS.TARGET.RANK[target_of_interest] ]] <- cut2(TEMP.DF[,TRAITS.TARGET.RANK[target_of_interest]], g = 2)
    cat(paste0("   > cross tabulation of ",TRAITS.TARGET.RANK[target_of_interest],"-stratum.\n"))
    show(table(TEMP.DF[[ TRAITS.TARGET.RANK[target_of_interest] ]]))
    
    cat(paste0("\n   > fitting the model for ",TRAITS.TARGET.RANK[target_of_interest],"-stratum.\n"))
    fit <- survfit(as.formula(paste0("y ~ ", TRAITS.TARGET.RANK[target_of_interest])), data = TEMP.DF)
    
    cat(paste0("\n   > make a Kaplan-Meier-shizzle...\n"))
    # make Kaplan-Meier curve and save it
    show(ggsurvplot(fit, data = TEMP.DF,
                    palette = c("#DB003F", "#1290D9"),
                    # palete = c("F59D10", "#DB003F", "#49A01D", "#1290D9"),
                    linetype = c(1,2),
                    # linetype = c(1,2,3,4),
                    # conf.int = FALSE, conf.int.fill = "#595A5C", conf.int.alpha = 0.1,
                    pval = FALSE, pval.method = FALSE, pval.size = 4,
                    risk.table = TRUE, risk.table.y.text = FALSE, tables.y.text.col = TRUE, fontsize = 4,
                    censor = FALSE,
                    legend = "right",
                    legend.title = paste0("",TRAITS.TARGET.RANK[target_of_interest],""),
                    legend.labs = c("low", "high"),
                    title = paste0("Risk of ",ep,""), xlab = "Time [years]", font.main = c(16, "bold", "black")))
    dev.copy2pdf(file = paste0(COX_loc,"/",
                               Today,".AERNASE.clin.hdac9.survival.",ep,".2G.",
                               TRAITS.TARGET.RANK[target_of_interest],".pdf"), width = 12, height = 10, onefile = FALSE)

    cat(paste0("\n   > perform the Cox-regression fashizzle and plot it...\n"))
    ### Do Cox-regression and plot it
    
    ### MODEL 2 adjusted for age, sex, hypertension, diabetes, smoking, LDL-C levels, lipid-lowering drugs, antiplatelet drugs, eGFR, BMI, history of CVD, level of stenosis
    cox = coxph(Surv(TEMP.DF[,eptime], event) ~ TEMP.DF[[ TRAITS.TARGET.RANK[target_of_interest] ]]+Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus + SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + MedHx_CVD + stenose, data = TEMP.DF)
    coxplot = coxph(Surv(TEMP.DF[,eptime], event) ~ strata(TEMP.DF[[ TRAITS.TARGET.RANK[target_of_interest] ]])+Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus + SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + MedHx_CVD + stenose, data = TEMP.DF)

  
    plot(survfit(coxplot), main = paste0("Cox proportional hazard of [",ep,"] per [",eptime,"]."),
         # ylim = c(0.2, 1), xlim = c(0,3), col = c("#595A5C", "#DB003F", "#1290D9"),
         ylim = c(0, 1), xlim = c(0,3), col = c("#DB003F", "#1290D9"),
         lty = c(1,2), lwd = 2,
         ylab = "Suvival probability", xlab = "FU time [years]",
         mark.time = FALSE, axes = FALSE, bty = "n")
    legend("topright",
           c("low", "high"),
           title = paste0("",TRAITS.TARGET.RANK[target_of_interest],""),
           col = c("#DB003F", "#1290D9"),
           lty = c(1,2), lwd = 2,
           bty = "n")
    axis(side = 1, at = seq(0, 3, by = 1))
    axis(side = 2, at = seq(0, 1, by = 0.2))
    dev.copy2pdf(file = paste0(COX_loc,"/",
                               Today,".AERNASE.clin.hdac9.Cox.",ep,".2G.",
                               # Today,".AERNASE.clin.hdac9.Cox.",ep,".4G.",
                               TRAITS.TARGET.RANK[target_of_interest],".MODEL2.pdf"), height = 12, width = 10, onefile = TRUE)

    show(summary(cox))

    cat(paste0("\n   > writing the Cox-regression fashizzle to Excel...\n"))

    COX.results.TEMP <- data.frame(matrix(NA, ncol = 12, nrow = 0))
    COX.results.TEMP[1,] = COX.STAT(cox, "AERNASE.clin.hdac9", ep, TRAITS.TARGET.RANK[target_of_interest])
    COX.results = rbind(COX.results, COX.results.TEMP)

  }
}
* Analyzing the effect of plaque target-of-interest on [epmajor.3years].
 - creating temporary SE for this work.
 - making a 'Surv' object and adding this to temporary dataframe.
 - making strata of each of the plaque target-of-interest and start survival analysis.
   > processing [HDAC9]; 1 out of 1 target-of-interest.
   > cross tabulation of HDAC9-stratum.

[ 0, 22) [22,449] 
     319      304 

   > fitting the model for HDAC9-stratum.

   > make a Kaplan-Meier-shizzle...

   > perform the Cox-regression fashizzle and plot it...
Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights,  :
  Loglik converged before variable  4,5,6,7,8,9,10,11,12,13,14,35,36,37 ; coefficient may be infinite. 
Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights,  :
  Loglik converged before variable  3,4,5,6,7,8,9,10,11,12,13,34,35,36 ; coefficient may be infinite. 

Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]] + 
    Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, data = TEMP.DF)

  n= 535, number of events= 65 
   (88 observations deleted due to missingness)

                                                                coef  exp(coef)   se(coef)      z Pr(>|z|)  
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][22,449] -1.070e-01  8.985e-01  2.602e-01 -0.411   0.6808  
Age                                                        4.732e-02  1.048e+00  1.856e-02  2.550   0.0108 *
Gendermale                                                 8.189e-01  2.268e+00  3.879e-01  2.111   0.0348 *
ORdate_year2002                                            1.536e+01  4.678e+06  1.019e+04  0.002   0.9988  
ORdate_year2003                                            1.429e+01  1.601e+06  1.019e+04  0.001   0.9989  
ORdate_year2004                                            1.475e+01  2.550e+06  1.019e+04  0.001   0.9988  
ORdate_year2005                                            1.523e+01  4.116e+06  1.019e+04  0.001   0.9988  
ORdate_year2006                                            1.520e+01  4.007e+06  1.019e+04  0.001   0.9988  
ORdate_year2007                                            1.422e+01  1.495e+06  1.019e+04  0.001   0.9989  
ORdate_year2008                                            1.528e+01  4.311e+06  1.019e+04  0.001   0.9988  
ORdate_year2009                                            1.418e+01  1.446e+06  1.019e+04  0.001   0.9989  
ORdate_year2010                                            1.508e+01  3.550e+06  1.019e+04  0.001   0.9988  
ORdate_year2011                                            1.440e+01  1.795e+06  1.019e+04  0.001   0.9989  
ORdate_year2012                                            1.498e+01  3.205e+06  1.019e+04  0.001   0.9988  
ORdate_year2013                                           -1.394e+00  2.480e-01  1.121e+04  0.000   0.9999  
ORdate_year2014                                           -9.861e-01  3.730e-01  1.441e+04  0.000   0.9999  
ORdate_year2015                                           -7.423e-01  4.760e-01  1.128e+04  0.000   0.9999  
ORdate_year2016                                                   NA         NA  0.000e+00     NA       NA  
ORdate_year2017                                                   NA         NA  0.000e+00     NA       NA  
ORdate_year2018                                                   NA         NA  0.000e+00     NA       NA  
ORdate_year2019                                                   NA         NA  0.000e+00     NA       NA  
Hypertension.compositeno                                  -6.272e-01  5.341e-01  5.432e-01 -1.155   0.2482  
Hypertension.compositeyes                                         NA         NA  0.000e+00     NA       NA  
DiabetesStatusDiabetes                                     6.858e-01  1.985e+00  2.800e-01  2.449   0.0143 *
SmokerStatusEx-smoker                                     -5.207e-01  5.941e-01  2.781e-01 -1.873   0.0611 .
SmokerStatusNever smoked                                  -8.051e-01  4.470e-01  4.407e-01 -1.827   0.0677 .
Med.Statin.LLDno                                           2.396e-01  1.271e+00  2.979e-01  0.804   0.4213  
Med.Statin.LLDyes                                                 NA         NA  0.000e+00     NA       NA  
Med.all.antiplateletno                                     1.650e-01  1.179e+00  4.249e-01  0.388   0.6978  
Med.all.antiplateletyes                                           NA         NA  0.000e+00     NA       NA  
GFR_MDRD                                                  -7.953e-03  9.921e-01  6.443e-03 -1.234   0.2170  
BMI                                                        5.476e-02  1.056e+00  3.163e-02  1.731   0.0834 .
MedHx_CVDNo                                               -5.580e-01  5.724e-01  2.995e-01 -1.863   0.0624 .
stenose0-49%                                               4.153e-01  1.515e+00  1.156e+04  0.000   1.0000  
stenose50-70%                                              1.610e+01  9.869e+06  1.019e+04  0.002   0.9987  
stenose70-90%                                              1.663e+01  1.669e+07  1.019e+04  0.002   0.9987  
stenose90-99%                                              1.671e+01  1.802e+07  1.019e+04  0.002   0.9987  
stenose100% (Occlusion)                                    1.043e+00  2.837e+00  1.113e+04  0.000   0.9999  
stenoseNA                                                         NA         NA  0.000e+00     NA       NA  
stenose50-99%                                                     NA         NA  0.000e+00     NA       NA  
stenose70-99%                                                     NA         NA  0.000e+00     NA       NA  
stenose99                                                         NA         NA  0.000e+00     NA       NA  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

                                                          exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][22,449] 8.985e-01  1.113e+00    0.5396     1.496
Age                                                       1.048e+00  9.538e-01    1.0110     1.087
Gendermale                                                2.268e+00  4.409e-01    1.0603     4.851
ORdate_year2002                                           4.678e+06  2.138e-07    0.0000       Inf
ORdate_year2003                                           1.601e+06  6.247e-07    0.0000       Inf
ORdate_year2004                                           2.550e+06  3.922e-07    0.0000       Inf
ORdate_year2005                                           4.116e+06  2.430e-07    0.0000       Inf
ORdate_year2006                                           4.007e+06  2.495e-07    0.0000       Inf
ORdate_year2007                                           1.495e+06  6.687e-07    0.0000       Inf
ORdate_year2008                                           4.311e+06  2.320e-07    0.0000       Inf
ORdate_year2009                                           1.446e+06  6.915e-07    0.0000       Inf
ORdate_year2010                                           3.550e+06  2.817e-07    0.0000       Inf
ORdate_year2011                                           1.795e+06  5.571e-07    0.0000       Inf
ORdate_year2012                                           3.205e+06  3.120e-07    0.0000       Inf
ORdate_year2013                                           2.480e-01  4.033e+00    0.0000       Inf
ORdate_year2014                                           3.730e-01  2.681e+00    0.0000       Inf
ORdate_year2015                                           4.760e-01  2.101e+00    0.0000       Inf
ORdate_year2016                                                  NA         NA        NA        NA
ORdate_year2017                                                  NA         NA        NA        NA
ORdate_year2018                                                  NA         NA        NA        NA
ORdate_year2019                                                  NA         NA        NA        NA
Hypertension.compositeno                                  5.341e-01  1.872e+00    0.1842     1.549
Hypertension.compositeyes                                        NA         NA        NA        NA
DiabetesStatusDiabetes                                    1.985e+00  5.037e-01    1.1468     3.437
SmokerStatusEx-smoker                                     5.941e-01  1.683e+00    0.3445     1.025
SmokerStatusNever smoked                                  4.470e-01  2.237e+00    0.1885     1.060
Med.Statin.LLDno                                          1.271e+00  7.870e-01    0.7087     2.279
Med.Statin.LLDyes                                                NA         NA        NA        NA
Med.all.antiplateletno                                    1.179e+00  8.479e-01    0.5128     2.712
Med.all.antiplateletyes                                          NA         NA        NA        NA
GFR_MDRD                                                  9.921e-01  1.008e+00    0.9796     1.005
BMI                                                       1.056e+00  9.467e-01    0.9928     1.124
MedHx_CVDNo                                               5.724e-01  1.747e+00    0.3183     1.029
stenose0-49%                                              1.515e+00  6.601e-01    0.0000       Inf
stenose50-70%                                             9.869e+06  1.013e-07    0.0000       Inf
stenose70-90%                                             1.669e+07  5.991e-08    0.0000       Inf
stenose90-99%                                             1.802e+07  5.550e-08    0.0000       Inf
stenose100% (Occlusion)                                   2.837e+00  3.525e-01    0.0000       Inf
stenoseNA                                                        NA         NA        NA        NA
stenose50-99%                                                    NA         NA        NA        NA
stenose70-99%                                                    NA         NA        NA        NA
stenose99                                                        NA         NA        NA        NA

Concordance= 0.764  (se = 0.025 )
Likelihood ratio test= 55.11  on 31 df,   p=0.005
Wald test            = 38.75  on 31 df,   p=0.2
Score (logrank) test = 53.61  on 31 df,   p=0.007


   > writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' HDAC9 ' and its association to ' epmajor.3years ' in ' AERNASE.clin.hdac9 '.
Collecting data.

We have collected the following:
Dataset used..............: AERNASE.clin.hdac9 
Outcome analyzed..........: epmajor.3years 
Protein...................: HDAC9 
Effect size...............: -0.10702 
Standard error............: 0.260153 
Odds ratio (effect size)..: 0.899 
Lower 95% CI..............: 0.54 
Upper 95% CI..............: 1.496 
T-value...................: -0.411374 
P-value...................: 0.6807986 
Sample size in model......: 535 
Number of events..........: 65 
* Analyzing the effect of plaque target-of-interest on [epstroke.3years].
 - creating temporary SE for this work.
 - making a 'Surv' object and adding this to temporary dataframe.
 - making strata of each of the plaque target-of-interest and start survival analysis.
   > processing [HDAC9]; 1 out of 1 target-of-interest.
   > cross tabulation of HDAC9-stratum.

[ 0, 22) [22,449] 
     319      304 

   > fitting the model for HDAC9-stratum.

   > make a Kaplan-Meier-shizzle...

   > perform the Cox-regression fashizzle and plot it...
Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights,  :
  Loglik converged before variable  4,5,6,7,8,9,10,12,13,14,35,36,37 ; coefficient may be infinite. 
Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights,  :
  Loglik converged before variable  3,4,5,6,7,8,9,11,12,13,34,35,36 ; coefficient may be infinite. 

Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]] + 
    Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, data = TEMP.DF)

  n= 535, number of events= 34 
   (88 observations deleted due to missingness)

                                                                coef  exp(coef)   se(coef)      z Pr(>|z|)  
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][22,449] -1.405e-01  8.689e-01  3.612e-01 -0.389   0.6973  
Age                                                        5.009e-02  1.051e+00  2.480e-02  2.020   0.0434 *
Gendermale                                                 4.427e-01  1.557e+00  4.682e-01  0.945   0.3444  
ORdate_year2002                                            1.745e+01  3.778e+07  4.162e+04  0.000   0.9997  
ORdate_year2003                                            1.639e+01  1.318e+07  4.162e+04  0.000   0.9997  
ORdate_year2004                                            1.676e+01  1.897e+07  4.162e+04  0.000   0.9997  
ORdate_year2005                                            1.730e+01  3.260e+07  4.162e+04  0.000   0.9997  
ORdate_year2006                                            1.782e+01  5.483e+07  4.162e+04  0.000   0.9997  
ORdate_year2007                                            1.574e+01  6.883e+06  4.162e+04  0.000   0.9997  
ORdate_year2008                                            1.732e+01  3.332e+07  4.162e+04  0.000   0.9997  
ORdate_year2009                                           -1.313e+00  2.691e-01  4.190e+04  0.000   1.0000  
ORdate_year2010                                            1.758e+01  4.300e+07  4.162e+04  0.000   0.9997  
ORdate_year2011                                            1.647e+01  1.420e+07  4.162e+04  0.000   0.9997  
ORdate_year2012                                            1.725e+01  3.089e+07  4.162e+04  0.000   0.9997  
ORdate_year2013                                           -1.102e+00  3.321e-01  4.614e+04  0.000   1.0000  
ORdate_year2014                                           -9.220e-01  3.977e-01  5.885e+04  0.000   1.0000  
ORdate_year2015                                           -9.160e-01  4.001e-01  4.734e+04  0.000   1.0000  
ORdate_year2016                                                   NA         NA  0.000e+00     NA       NA  
ORdate_year2017                                                   NA         NA  0.000e+00     NA       NA  
ORdate_year2018                                                   NA         NA  0.000e+00     NA       NA  
ORdate_year2019                                                   NA         NA  0.000e+00     NA       NA  
Hypertension.compositeno                                  -1.841e-01  8.319e-01  6.509e-01 -0.283   0.7773  
Hypertension.compositeyes                                         NA         NA  0.000e+00     NA       NA  
DiabetesStatusDiabetes                                     3.340e-01  1.397e+00  4.180e-01  0.799   0.4243  
SmokerStatusEx-smoker                                     -5.879e-01  5.555e-01  3.743e-01 -1.571   0.1162  
SmokerStatusNever smoked                                  -1.568e+00  2.085e-01  7.651e-01 -2.049   0.0404 *
Med.Statin.LLDno                                           2.917e-01  1.339e+00  4.067e-01  0.717   0.4733  
Med.Statin.LLDyes                                                 NA         NA  0.000e+00     NA       NA  
Med.all.antiplateletno                                    -6.841e-02  9.339e-01  6.452e-01 -0.106   0.9156  
Med.all.antiplateletyes                                           NA         NA  0.000e+00     NA       NA  
GFR_MDRD                                                   6.356e-03  1.006e+00  8.677e-03  0.732   0.4639  
BMI                                                        9.627e-02  1.101e+00  4.095e-02  2.351   0.0187 *
MedHx_CVDNo                                               -4.748e-01  6.220e-01  4.047e-01 -1.173   0.2406  
stenose0-49%                                               1.588e+00  4.896e+00  4.579e+04  0.000   1.0000  
stenose50-70%                                              1.952e+01  2.998e+08  4.161e+04  0.000   0.9996  
stenose70-90%                                              1.942e+01  2.725e+08  4.161e+04  0.000   0.9996  
stenose90-99%                                              1.951e+01  2.959e+08  4.161e+04  0.000   0.9996  
stenose100% (Occlusion)                                    1.617e+00  5.039e+00  4.436e+04  0.000   1.0000  
stenoseNA                                                         NA         NA  0.000e+00     NA       NA  
stenose50-99%                                                     NA         NA  0.000e+00     NA       NA  
stenose70-99%                                                     NA         NA  0.000e+00     NA       NA  
stenose99                                                         NA         NA  0.000e+00     NA       NA  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

                                                          exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][22,449] 8.689e-01  1.151e+00   0.42810    1.7636
Age                                                       1.051e+00  9.511e-01   1.00148    1.1037
Gendermale                                                1.557e+00  6.423e-01   0.62185    3.8978
ORdate_year2002                                           3.778e+07  2.647e-08   0.00000       Inf
ORdate_year2003                                           1.318e+07  7.589e-08   0.00000       Inf
ORdate_year2004                                           1.897e+07  5.272e-08   0.00000       Inf
ORdate_year2005                                           3.260e+07  3.068e-08   0.00000       Inf
ORdate_year2006                                           5.483e+07  1.824e-08   0.00000       Inf
ORdate_year2007                                           6.883e+06  1.453e-07   0.00000       Inf
ORdate_year2008                                           3.332e+07  3.001e-08   0.00000       Inf
ORdate_year2009                                           2.691e-01  3.716e+00   0.00000       Inf
ORdate_year2010                                           4.300e+07  2.325e-08   0.00000       Inf
ORdate_year2011                                           1.420e+07  7.044e-08   0.00000       Inf
ORdate_year2012                                           3.089e+07  3.237e-08   0.00000       Inf
ORdate_year2013                                           3.321e-01  3.011e+00   0.00000       Inf
ORdate_year2014                                           3.977e-01  2.514e+00   0.00000       Inf
ORdate_year2015                                           4.001e-01  2.499e+00   0.00000       Inf
ORdate_year2016                                                  NA         NA        NA        NA
ORdate_year2017                                                  NA         NA        NA        NA
ORdate_year2018                                                  NA         NA        NA        NA
ORdate_year2019                                                  NA         NA        NA        NA
Hypertension.compositeno                                  8.319e-01  1.202e+00   0.23227    2.9793
Hypertension.compositeyes                                        NA         NA        NA        NA
DiabetesStatusDiabetes                                    1.397e+00  7.161e-01   0.61551    3.1686
SmokerStatusEx-smoker                                     5.555e-01  1.800e+00   0.26673    1.1568
SmokerStatusNever smoked                                  2.085e-01  4.797e+00   0.04654    0.9339
Med.Statin.LLDno                                          1.339e+00  7.470e-01   0.60326    2.9706
Med.Statin.LLDyes                                                NA         NA        NA        NA
Med.all.antiplateletno                                    9.339e-01  1.071e+00   0.26371    3.3071
Med.all.antiplateletyes                                          NA         NA        NA        NA
GFR_MDRD                                                  1.006e+00  9.937e-01   0.98941    1.0236
BMI                                                       1.101e+00  9.082e-01   1.01613    1.1931
MedHx_CVDNo                                               6.220e-01  1.608e+00   0.28141    1.3748
stenose0-49%                                              4.896e+00  2.043e-01   0.00000       Inf
stenose50-70%                                             2.998e+08  3.335e-09   0.00000       Inf
stenose70-90%                                             2.725e+08  3.669e-09   0.00000       Inf
stenose90-99%                                             2.959e+08  3.379e-09   0.00000       Inf
stenose100% (Occlusion)                                   5.039e+00  1.985e-01   0.00000       Inf
stenoseNA                                                        NA         NA        NA        NA
stenose50-99%                                                    NA         NA        NA        NA
stenose70-99%                                                    NA         NA        NA        NA
stenose99                                                        NA         NA        NA        NA

Concordance= 0.795  (se = 0.031 )
Likelihood ratio test= 39.43  on 31 df,   p=0.1
Wald test            = 16.75  on 31 df,   p=1
Score (logrank) test = 35.86  on 31 df,   p=0.3


   > writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' HDAC9 ' and its association to ' epstroke.3years ' in ' AERNASE.clin.hdac9 '.
Collecting data.

We have collected the following:
Dataset used..............: AERNASE.clin.hdac9 
Outcome analyzed..........: epstroke.3years 
Protein...................: HDAC9 
Effect size...............: -0.140508 
Standard error............: 0.361176 
Odds ratio (effect size)..: 0.869 
Lower 95% CI..............: 0.428 
Upper 95% CI..............: 1.764 
T-value...................: -0.38903 
P-value...................: 0.6972542 
Sample size in model......: 535 
Number of events..........: 34 
* Analyzing the effect of plaque target-of-interest on [epcoronary.3years].
 - creating temporary SE for this work.
 - making a 'Surv' object and adding this to temporary dataframe.
 - making strata of each of the plaque target-of-interest and start survival analysis.
   > processing [HDAC9]; 1 out of 1 target-of-interest.
   > cross tabulation of HDAC9-stratum.

[ 0, 22) [22,449] 
     319      304 

   > fitting the model for HDAC9-stratum.

   > make a Kaplan-Meier-shizzle...

   > perform the Cox-regression fashizzle and plot it...
Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights,  :
  Loglik converged before variable  4,5,6,7,8,9,10,11,12,13,14,16,17,35,36,37,38 ; coefficient may be infinite. 
Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights,  :
  Loglik converged before variable  3,4,5,6,7,8,9,10,11,12,13,35,36,37 ; coefficient may be infinite. 

Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]] + 
    Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, data = TEMP.DF)

  n= 535, number of events= 43 
   (88 observations deleted due to missingness)

                                                                coef  exp(coef)   se(coef)      z Pr(>|z|)   
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][22,449] -1.546e-01  8.567e-01  3.205e-01 -0.482  0.62953   
Age                                                       -1.299e-03  9.987e-01  2.280e-02 -0.057  0.95459   
Gendermale                                                 1.273e+00  3.572e+00  5.389e-01  2.362  0.01816 * 
ORdate_year2002                                            1.746e+01  3.822e+07  3.427e+04  0.001  0.99959   
ORdate_year2003                                            1.707e+01  2.601e+07  3.427e+04  0.000  0.99960   
ORdate_year2004                                            1.624e+01  1.132e+07  3.427e+04  0.000  0.99962   
ORdate_year2005                                            1.722e+01  3.015e+07  3.427e+04  0.001  0.99960   
ORdate_year2006                                            1.702e+01  2.464e+07  3.427e+04  0.000  0.99960   
ORdate_year2007                                            1.684e+01  2.054e+07  3.427e+04  0.000  0.99961   
ORdate_year2008                                            1.697e+01  2.344e+07  3.427e+04  0.000  0.99960   
ORdate_year2009                                            1.670e+01  1.784e+07  3.427e+04  0.000  0.99961   
ORdate_year2010                                            1.563e+01  6.123e+06  3.427e+04  0.000  0.99964   
ORdate_year2011                                            1.602e+01  9.103e+06  3.427e+04  0.000  0.99963   
ORdate_year2012                                            1.654e+01  1.525e+07  3.427e+04  0.000  0.99961   
ORdate_year2013                                           -1.790e+00  1.669e-01  3.758e+04  0.000  0.99996   
ORdate_year2014                                           -5.827e-01  5.584e-01  4.847e+04  0.000  0.99999   
ORdate_year2015                                           -1.527e-01  8.584e-01  3.747e+04  0.000  1.00000   
ORdate_year2016                                                   NA         NA  0.000e+00     NA       NA   
ORdate_year2017                                                   NA         NA  0.000e+00     NA       NA   
ORdate_year2018                                                   NA         NA  0.000e+00     NA       NA   
ORdate_year2019                                                   NA         NA  0.000e+00     NA       NA   
Hypertension.compositeno                                  -1.679e+00  1.865e-01  1.029e+00 -1.631  0.10283   
Hypertension.compositeyes                                         NA         NA  0.000e+00     NA       NA   
DiabetesStatusDiabetes                                     3.192e-01  1.376e+00  3.502e-01  0.912  0.36194   
SmokerStatusEx-smoker                                     -3.283e-01  7.201e-01  3.536e-01 -0.929  0.35308   
SmokerStatusNever smoked                                   9.937e-02  1.104e+00  4.722e-01  0.210  0.83332   
Med.Statin.LLDno                                          -4.074e-01  6.654e-01  4.369e-01 -0.932  0.35113   
Med.Statin.LLDyes                                                 NA         NA  0.000e+00     NA       NA   
Med.all.antiplateletno                                     1.760e-02  1.018e+00  5.674e-01  0.031  0.97526   
Med.all.antiplateletyes                                           NA         NA  0.000e+00     NA       NA   
GFR_MDRD                                                  -1.438e-02  9.857e-01  8.199e-03 -1.754  0.07940 . 
BMI                                                        1.769e-02  1.018e+00  4.199e-02  0.421  0.67352   
MedHx_CVDNo                                               -1.199e+00  3.016e-01  4.254e-01 -2.818  0.00484 **
stenose0-49%                                              -1.731e+00  1.771e-01  3.919e+04  0.000  0.99996   
stenose50-70%                                             -6.636e-01  5.150e-01  3.469e+04  0.000  0.99998   
stenose70-90%                                              1.726e+01  3.129e+07  3.427e+04  0.001  0.99960   
stenose90-99%                                              1.708e+01  2.616e+07  3.427e+04  0.000  0.99960   
stenose100% (Occlusion)                                   -5.124e-01  5.990e-01  3.719e+04  0.000  0.99999   
stenoseNA                                                         NA         NA  0.000e+00     NA       NA   
stenose50-99%                                                     NA         NA  0.000e+00     NA       NA   
stenose70-99%                                                     NA         NA  0.000e+00     NA       NA   
stenose99                                                         NA         NA  0.000e+00     NA       NA   
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

                                                          exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][22,449] 8.567e-01  1.167e+00    0.4571    1.6057
Age                                                       9.987e-01  1.001e+00    0.9551    1.0443
Gendermale                                                3.572e+00  2.800e-01    1.2421   10.2700
ORdate_year2002                                           3.822e+07  2.617e-08    0.0000       Inf
ORdate_year2003                                           2.601e+07  3.845e-08    0.0000       Inf
ORdate_year2004                                           1.132e+07  8.835e-08    0.0000       Inf
ORdate_year2005                                           3.015e+07  3.317e-08    0.0000       Inf
ORdate_year2006                                           2.464e+07  4.059e-08    0.0000       Inf
ORdate_year2007                                           2.054e+07  4.869e-08    0.0000       Inf
ORdate_year2008                                           2.344e+07  4.266e-08    0.0000       Inf
ORdate_year2009                                           1.784e+07  5.606e-08    0.0000       Inf
ORdate_year2010                                           6.123e+06  1.633e-07    0.0000       Inf
ORdate_year2011                                           9.103e+06  1.099e-07    0.0000       Inf
ORdate_year2012                                           1.525e+07  6.557e-08    0.0000       Inf
ORdate_year2013                                           1.669e-01  5.992e+00    0.0000       Inf
ORdate_year2014                                           5.584e-01  1.791e+00    0.0000       Inf
ORdate_year2015                                           8.584e-01  1.165e+00    0.0000       Inf
ORdate_year2016                                                  NA         NA        NA        NA
ORdate_year2017                                                  NA         NA        NA        NA
ORdate_year2018                                                  NA         NA        NA        NA
ORdate_year2019                                                  NA         NA        NA        NA
Hypertension.compositeno                                  1.865e-01  5.362e+00    0.0248    1.4026
Hypertension.compositeyes                                        NA         NA        NA        NA
DiabetesStatusDiabetes                                    1.376e+00  7.267e-01    0.6928    2.7333
SmokerStatusEx-smoker                                     7.201e-01  1.389e+00    0.3601    1.4400
SmokerStatusNever smoked                                  1.104e+00  9.054e-01    0.4378    2.7866
Med.Statin.LLDno                                          6.654e-01  1.503e+00    0.2826    1.5667
Med.Statin.LLDyes                                                NA         NA        NA        NA
Med.all.antiplateletno                                    1.018e+00  9.826e-01    0.3347    3.0945
Med.all.antiplateletyes                                          NA         NA        NA        NA
GFR_MDRD                                                  9.857e-01  1.014e+00    0.9700    1.0017
BMI                                                       1.018e+00  9.825e-01    0.9374    1.1051
MedHx_CVDNo                                               3.016e-01  3.316e+00    0.1310    0.6943
stenose0-49%                                              1.771e-01  5.646e+00    0.0000       Inf
stenose50-70%                                             5.150e-01  1.942e+00    0.0000       Inf
stenose70-90%                                             3.129e+07  3.196e-08    0.0000       Inf
stenose90-99%                                             2.616e+07  3.822e-08    0.0000       Inf
stenose100% (Occlusion)                                   5.990e-01  1.669e+00    0.0000       Inf
stenoseNA                                                        NA         NA        NA        NA
stenose50-99%                                                    NA         NA        NA        NA
stenose70-99%                                                    NA         NA        NA        NA
stenose99                                                        NA         NA        NA        NA

Concordance= 0.778  (se = 0.027 )
Likelihood ratio test= 45.51  on 31 df,   p=0.04
Wald test            = 23.94  on 31 df,   p=0.8
Score (logrank) test = 37.26  on 31 df,   p=0.2


   > writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' HDAC9 ' and its association to ' epcoronary.3years ' in ' AERNASE.clin.hdac9 '.
Collecting data.

We have collected the following:
Dataset used..............: AERNASE.clin.hdac9 
Outcome analyzed..........: epcoronary.3years 
Protein...................: HDAC9 
Effect size...............: -0.15461 
Standard error............: 0.320506 
Odds ratio (effect size)..: 0.857 
Lower 95% CI..............: 0.457 
Upper 95% CI..............: 1.606 
T-value...................: -0.482393 
P-value...................: 0.6295269 
Sample size in model......: 535 
Number of events..........: 43 
* Analyzing the effect of plaque target-of-interest on [epcvdeath.3years].
 - creating temporary SE for this work.
 - making a 'Surv' object and adding this to temporary dataframe.
 - making strata of each of the plaque target-of-interest and start survival analysis.
   > processing [HDAC9]; 1 out of 1 target-of-interest.
   > cross tabulation of HDAC9-stratum.

[ 0, 22) [22,449] 
     319      304 

   > fitting the model for HDAC9-stratum.

   > make a Kaplan-Meier-shizzle...

   > perform the Cox-regression fashizzle and plot it...
Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights,  :
  Loglik converged before variable  4,5,6,7,8,9,10,11,12,13,14,15,16,17,22,34,35,36,37,38 ; coefficient may be infinite. 
Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights,  :
  Loglik converged before variable  3,4,5,6,7,8,9,10,11,13,16,21,33,34,35,36,37 ; coefficient may be infinite. 

Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]] + 
    Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, data = TEMP.DF)

  n= 535, number of events= 23 
   (88 observations deleted due to missingness)

                                                                coef  exp(coef)   se(coef)      z Pr(>|z|)  
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][22,449] -2.771e-01  7.580e-01  4.600e-01 -0.602   0.5469  
Age                                                        7.215e-02  1.075e+00  3.477e-02  2.075   0.0380 *
Gendermale                                                 6.159e-01  1.851e+00  6.573e-01  0.937   0.3487  
ORdate_year2002                                            1.820e+01  8.034e+07  7.481e+04  0.000   0.9998  
ORdate_year2003                                            1.684e+01  2.065e+07  7.481e+04  0.000   0.9998  
ORdate_year2004                                            1.699e+01  2.393e+07  7.481e+04  0.000   0.9998  
ORdate_year2005                                            1.775e+01  5.101e+07  7.481e+04  0.000   0.9998  
ORdate_year2006                                            1.673e+01  1.847e+07  7.481e+04  0.000   0.9998  
ORdate_year2007                                            1.709e+01  2.642e+07  7.481e+04  0.000   0.9998  
ORdate_year2008                                            1.726e+01  3.131e+07  7.481e+04  0.000   0.9998  
ORdate_year2009                                            1.691e+01  2.214e+07  7.481e+04  0.000   0.9998  
ORdate_year2010                                            1.766e+01  4.657e+07  7.481e+04  0.000   0.9998  
ORdate_year2011                                           -1.550e+00  2.123e-01  7.545e+04  0.000   1.0000  
ORdate_year2012                                           -1.213e+00  2.974e-01  7.591e+04  0.000   1.0000  
ORdate_year2013                                           -1.558e+00  2.105e-01  8.214e+04  0.000   1.0000  
ORdate_year2014                                           -1.108e+00  3.303e-01  1.058e+05  0.000   1.0000  
ORdate_year2015                                           -1.084e+00  3.383e-01  7.997e+04  0.000   1.0000  
ORdate_year2016                                                   NA         NA  0.000e+00     NA       NA  
ORdate_year2017                                                   NA         NA  0.000e+00     NA       NA  
ORdate_year2018                                                   NA         NA  0.000e+00     NA       NA  
ORdate_year2019                                                   NA         NA  0.000e+00     NA       NA  
Hypertension.compositeno                                  -1.820e+01  1.248e-08  4.924e+03 -0.004   0.9971  
Hypertension.compositeyes                                         NA         NA  0.000e+00     NA       NA  
DiabetesStatusDiabetes                                     9.437e-01  2.570e+00  4.835e-01  1.952   0.0510 .
SmokerStatusEx-smoker                                     -6.808e-01  5.062e-01  4.963e-01 -1.372   0.1701  
SmokerStatusNever smoked                                  -1.101e+00  3.325e-01  8.341e-01 -1.320   0.1868  
Med.Statin.LLDno                                           3.260e-01  1.385e+00  4.851e-01  0.672   0.5015  
Med.Statin.LLDyes                                                 NA         NA  0.000e+00     NA       NA  
Med.all.antiplateletno                                     1.089e+00  2.970e+00  5.963e-01  1.826   0.0679 .
Med.all.antiplateletyes                                           NA         NA  0.000e+00     NA       NA  
GFR_MDRD                                                  -2.453e-02  9.758e-01  1.207e-02 -2.032   0.0422 *
BMI                                                        3.188e-02  1.032e+00  6.026e-02  0.529   0.5968  
MedHx_CVDNo                                               -5.370e-01  5.845e-01  5.478e-01 -0.980   0.3269  
stenose0-49%                                              -3.568e+01  3.197e-16  8.202e+04  0.000   0.9997  
stenose50-70%                                             -1.702e+01  4.059e-08  7.607e+04  0.000   0.9998  
stenose70-90%                                             -1.733e+01  2.984e-08  7.607e+04  0.000   0.9998  
stenose90-99%                                             -1.703e+01  4.016e-08  7.607e+04  0.000   0.9998  
stenose100% (Occlusion)                                   -3.299e+01  4.695e-15  7.795e+04  0.000   0.9997  
stenoseNA                                                         NA         NA  0.000e+00     NA       NA  
stenose50-99%                                                     NA         NA  0.000e+00     NA       NA  
stenose70-99%                                                     NA         NA  0.000e+00     NA       NA  
stenose99                                                         NA         NA  0.000e+00     NA       NA  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

                                                          exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][22,449] 7.580e-01  1.319e+00   0.30766    1.8673
Age                                                       1.075e+00  9.304e-01   1.00401    1.1506
Gendermale                                                1.851e+00  5.401e-01   0.51053    6.7140
ORdate_year2002                                           8.034e+07  1.245e-08   0.00000       Inf
ORdate_year2003                                           2.065e+07  4.844e-08   0.00000       Inf
ORdate_year2004                                           2.393e+07  4.179e-08   0.00000       Inf
ORdate_year2005                                           5.101e+07  1.960e-08   0.00000       Inf
ORdate_year2006                                           1.847e+07  5.416e-08   0.00000       Inf
ORdate_year2007                                           2.642e+07  3.785e-08   0.00000       Inf
ORdate_year2008                                           3.131e+07  3.194e-08   0.00000       Inf
ORdate_year2009                                           2.214e+07  4.516e-08   0.00000       Inf
ORdate_year2010                                           4.657e+07  2.147e-08   0.00000       Inf
ORdate_year2011                                           2.123e-01  4.710e+00   0.00000       Inf
ORdate_year2012                                           2.974e-01  3.363e+00   0.00000       Inf
ORdate_year2013                                           2.105e-01  4.750e+00   0.00000       Inf
ORdate_year2014                                           3.303e-01  3.027e+00   0.00000       Inf
ORdate_year2015                                           3.383e-01  2.956e+00   0.00000       Inf
ORdate_year2016                                                  NA         NA        NA        NA
ORdate_year2017                                                  NA         NA        NA        NA
ORdate_year2018                                                  NA         NA        NA        NA
ORdate_year2019                                                  NA         NA        NA        NA
Hypertension.compositeno                                  1.248e-08  8.016e+07   0.00000       Inf
Hypertension.compositeyes                                        NA         NA        NA        NA
DiabetesStatusDiabetes                                    2.570e+00  3.892e-01   0.99604    6.6287
SmokerStatusEx-smoker                                     5.062e-01  1.975e+00   0.19137    1.3390
SmokerStatusNever smoked                                  3.325e-01  3.007e+00   0.06484    1.7051
Med.Statin.LLDno                                          1.385e+00  7.218e-01   0.53540    3.5853
Med.Statin.LLDyes                                                NA         NA        NA        NA
Med.all.antiplateletno                                    2.970e+00  3.367e-01   0.92302    9.5590
Med.all.antiplateletyes                                          NA         NA        NA        NA
GFR_MDRD                                                  9.758e-01  1.025e+00   0.95295    0.9991
BMI                                                       1.032e+00  9.686e-01   0.91739    1.1618
MedHx_CVDNo                                               5.845e-01  1.711e+00   0.19978    1.7102
stenose0-49%                                              3.197e-16  3.128e+15   0.00000       Inf
stenose50-70%                                             4.059e-08  2.464e+07   0.00000       Inf
stenose70-90%                                             2.984e-08  3.351e+07   0.00000       Inf
stenose90-99%                                             4.016e-08  2.490e+07   0.00000       Inf
stenose100% (Occlusion)                                   4.695e-15  2.130e+14   0.00000       Inf
stenoseNA                                                        NA         NA        NA        NA
stenose50-99%                                                    NA         NA        NA        NA
stenose70-99%                                                    NA         NA        NA        NA
stenose99                                                        NA         NA        NA        NA

Concordance= 0.852  (se = 0.033 )
Likelihood ratio test= 42.92  on 31 df,   p=0.08
Wald test            = 21.37  on 31 df,   p=0.9
Score (logrank) test = 39.69  on 31 df,   p=0.1


   > writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' HDAC9 ' and its association to ' epcvdeath.3years ' in ' AERNASE.clin.hdac9 '.
Collecting data.

We have collected the following:
Dataset used..............: AERNASE.clin.hdac9 
Outcome analyzed..........: epcvdeath.3years 
Protein...................: HDAC9 
Effect size...............: -0.277124 
Standard error............: 0.46002 
Odds ratio (effect size)..: 0.758 
Lower 95% CI..............: 0.308 
Upper 95% CI..............: 1.867 
T-value...................: -0.602416 
P-value...................: 0.546897 
Sample size in model......: 535 
Number of events..........: 23 

cat("- Edit the column names...\n")
- Edit the column names...
colnames(COX.results) = c("Dataset", "Outcome", "CpG",
                          "Beta", "s.e.m.",
                          "HR", "low95CI", "up95CI",
                          "Z-value", "P-value", "SampleSize", "N_events")

cat("- Correct the variable types...\n")
- Correct the variable types...
COX.results$Beta <- as.numeric(COX.results$Beta)
COX.results$s.e.m. <- as.numeric(COX.results$s.e.m.)
COX.results$HR <- as.numeric(COX.results$HR)
COX.results$low95CI <- as.numeric(COX.results$low95CI)
COX.results$up95CI <- as.numeric(COX.results$up95CI)
COX.results$`Z-value` <- as.numeric(COX.results$`Z-value`)
COX.results$`P-value` <- as.numeric(COX.results$`P-value`)
COX.results$SampleSize <- as.numeric(COX.results$SampleSize)
COX.results$N_events <- as.numeric(COX.results$N_events)

AERNASE.clin.hdac9.COX.results <- COX.results

# Save the data
cat("- Writing results to Excel-file...\n")
- Writing results to Excel-file...
head.style <- createStyle(textDecoration = "BOLD")
write.xlsx(AERNASE.clin.hdac9.COX.results,
           file = paste0(OUT_loc, "/",Today,".AERNASE.clin.hdac9.Cox.2G.MODEL2.xlsx"),
           creator = "Sander W. van der Laan",
           sheetName = "Results", headerStyle = head.style,
           rowNames = FALSE, colNames = TRUE, overwrite = TRUE)

# Removing intermediates
cat("- Removing intermediate files...\n")
- Removing intermediate files...
rm(TEMP.DF, target_of_interest, fit, cox, coxplot, COX.results, COX.results.TEMP, head.style, AERNASE.clin.hdac9.COX.results)

30-days follow-up

Model 1
# Set up a dataframe to receive results
COX.results <- data.frame(matrix(NA, ncol = 12, nrow = 0))

# Looping over each target_of_interest/endpoint/time combination
for (i in 1:length(times30)){
  eptime = times30[i]
  ep = endpoints30[i]
  cat(paste0("* Analyzing the effect of plaque target-of-interest on [",ep,"].\n"))
  cat(" - creating temporary SE for this work.\n")
  TEMP.DF = as.data.frame(AERNASE.clin.hdac9)
  cat(" - making a 'Surv' object and adding this to temporary dataframe.\n")
  TEMP.DF$event <- as.integer(TEMP.DF[,ep])
  TEMP.DF$y <- Surv(time = TEMP.DF[,eptime], event = TEMP.DF$event)
  cat(" - making strata of each of the plaque target-of-interest and start survival analysis.\n")
  
  for (target_of_interest in 1:length(TRAITS.TARGET.RANK)){
    cat(paste0("   > processing [",TRAITS.TARGET.RANK[target_of_interest],"]; ",target_of_interest," out of ",length(TRAITS.TARGET.RANK)," target-of-interest.\n"))
    # splitting into two groups
    TEMP.DF[[ TRAITS.TARGET.RANK[target_of_interest] ]] <- cut2(TEMP.DF[,TRAITS.TARGET.RANK[target_of_interest]], g = 2)
    cat(paste0("   > cross tabulation of ",TRAITS.TARGET.RANK[target_of_interest],"-stratum.\n"))
    show(table(TEMP.DF[[ TRAITS.TARGET.RANK[target_of_interest] ]]))
    
    cat(paste0("\n   > fitting the model for ",TRAITS.TARGET.RANK[target_of_interest],"-stratum.\n"))
    fit <- survfit(as.formula(paste0("y ~ ", TRAITS.TARGET.RANK[target_of_interest])), data = TEMP.DF)
    
    cat(paste0("\n   > make a Kaplan-Meier-shizzle...\n"))
    # make Kaplan-Meier curve and save it
    show(ggsurvplot(fit, data = TEMP.DF,
                    palette = c("#DB003F", "#1290D9"),
                    # palete = c("F59D10", "#DB003F", "#49A01D", "#1290D9"),
                    linetype = c(1,2),
                    ylim = c(0.75, 1),
                    # linetype = c(1,2,3,4),
                    # conf.int = FALSE, conf.int.fill = "#595A5C", conf.int.alpha = 0.1,
                    pval = FALSE, pval.method = FALSE, pval.size = 4,
                    risk.table = TRUE, risk.table.y.text = FALSE, tables.y.text.col = TRUE, fontsize = 4,
                    censor = FALSE,
                    legend = "right",
                    legend.title = paste0("",TRAITS.TARGET.RANK[target_of_interest],""),
                    legend.labs = c("low", "high"),
                    title = paste0("Risk of ",ep,""), xlab = "Time [days]", font.main = c(16, "bold", "black")))
    dev.copy2pdf(file = paste0(COX_loc,"/",
                               Today,".AERNASE.clin.hdac9.survival.",ep,".2G.",
                               TRAITS.TARGET.RANK[target_of_interest],".30days.pdf"), width = 12, height = 10, onefile = FALSE)

    cat(paste0("\n   > perform the Cox-regression fashizzle and plot it...\n"))
    ### Do Cox-regression and plot it
    
    ### MODEL 1 (Simple model)
    cox = coxph(Surv(TEMP.DF[,eptime], event) ~ TEMP.DF[[ TRAITS.TARGET.RANK[target_of_interest] ]]+Age+Gender + ORdate_year, data = TEMP.DF)
    coxplot = coxph(Surv(TEMP.DF[,eptime], event) ~ strata(TEMP.DF[[ TRAITS.TARGET.RANK[target_of_interest] ]])+Age+Gender + ORdate_year, data = TEMP.DF)

    plot(survfit(coxplot), main = paste0("Cox proportional hazard of [",ep,"] per [",eptime,"]."),
         ylim = c(0.75, 1), xlim = c(0,3), col = c("#595A5C", "#DB003F", "#1290D9"),
         # ylim = c(0, 1), xlim = c(0,3), col = c("#DB003F", "#1290D9"),
         lty = c(1,2), lwd = 2,
         ylab = "Suvival probability", xlab = "FU time [days]",
         mark.time = FALSE, axes = FALSE, bty = "n")
    legend("topright",
           c("low", "high"),
           title = paste0("",TRAITS.TARGET.RANK[target_of_interest],""),
           col = c("#DB003F", "#1290D9"),
           lty = c(1,2), lwd = 2,
           bty = "n")
    axis(side = 1, at = seq(0, 3, by = 1))
    axis(side = 2, at = seq(0, 1, by = 0.2))
    dev.copy2pdf(file = paste0(COX_loc,"/",
                               Today,".AERNASE.clin.hdac9.Cox.",ep,".2G.",
                               # Today,".AERNASE.clin.hdac9.Cox.",ep,".4G.",
                               TRAITS.TARGET.RANK[target_of_interest],".MODEL1.30days.pdf"), height = 12, width = 10, onefile = TRUE)
    show(summary(cox))

    cat(paste0("\n   > writing the Cox-regression fashizzle to Excel...\n"))

    COX.results.TEMP <- data.frame(matrix(NA, ncol = 12, nrow = 0))
    COX.results.TEMP[1,] = COX.STAT(cox, "AERNASE.clin.hdac9", ep, TRAITS.TARGET.RANK[target_of_interest])
    COX.results = rbind(COX.results, COX.results.TEMP)

  }
}

cat("- Edit the column names...\n")
colnames(COX.results) = c("Dataset", "Outcome", "CpG",
                          "Beta", "s.e.m.",
                          "HR", "low95CI", "up95CI",
                          "Z-value", "P-value", "SampleSize", "N_events")

cat("- Correct the variable types...\n")
COX.results$Beta <- as.numeric(COX.results$Beta)
COX.results$s.e.m. <- as.numeric(COX.results$s.e.m.)
COX.results$HR <- as.numeric(COX.results$HR)
COX.results$low95CI <- as.numeric(COX.results$low95CI)
COX.results$up95CI <- as.numeric(COX.results$up95CI)
COX.results$`Z-value` <- as.numeric(COX.results$`Z-value`)
COX.results$`P-value` <- as.numeric(COX.results$`P-value`)
COX.results$SampleSize <- as.numeric(COX.results$SampleSize)
COX.results$N_events <- as.numeric(COX.results$N_events)

AERNASE.clin.hdac9.COX.results <- COX.results

# Save the data
library(openxlsx)
cat("- Writing results to Excel-file...\n")
head.style <- createStyle(textDecoration = "BOLD")
write.xlsx(AERNASE.clin.hdac9.COX.results,
           file = paste0(OUT_loc, "/",Today,".AERNASE.clin.hdac9.Cox.2G.MODEL1.30days.xlsx"),
           creator = "Sander W. van der Laan",
           sheetName = "Results", headerStyle = head.style,
           rowNames = FALSE, colnames = TRUE, overwrite = TRUE)

# Removing intermediates
cat("- Removing intermediate files...\n")
rm(TEMP.DF, target_of_interest, fit, cox, coxplot, COX.results, COX.results.TEMP, head.style, AERNASE.clin.hdac9.COX.results)
Model 2
# Set up a dataframe to receive results
COX.results <- data.frame(matrix(NA, ncol = 12, nrow = 0))

# Looping over each target_of_interest/endpoint/time combination
for (i in 1:length(times30)){
  eptime = times30[i]
  ep = endpoints30[i]
  cat(paste0("* Analyzing the effect of plaque target-of-interest on [",ep,"].\n"))
  cat(" - creating temporary SE for this work.\n")
  TEMP.DF = as.data.frame(AERNASE.clin.hdac9)
  cat(" - making a 'Surv' object and adding this to temporary dataframe.\n")
  TEMP.DF$event <- as.integer(TEMP.DF[,ep])
  #as.integer(TEMP.DF[,ep] == "Excluded")

  TEMP.DF$y <- Surv(time = TEMP.DF[,eptime], event = TEMP.DF$event)
  cat(" - making strata of each of the plaque target-of-interest and start survival analysis.\n")
  
  for (target_of_interest in 1:length(TRAITS.TARGET.RANK)){
    cat(paste0("   > processing [",TRAITS.TARGET.RANK[target_of_interest],"]; ",target_of_interest," out of ",length(TRAITS.TARGET.RANK)," target-of-interest.\n"))
    # splitting into two groups
    TEMP.DF[[ TRAITS.TARGET.RANK[target_of_interest] ]] <- cut2(TEMP.DF[,TRAITS.TARGET.RANK[target_of_interest]], g = 2)
    cat(paste0("   > cross tabulation of ",TRAITS.TARGET.RANK[target_of_interest],"-stratum.\n"))
    show(table(TEMP.DF[[ TRAITS.TARGET.RANK[target_of_interest] ]]))
    
    cat(paste0("\n   > fitting the model for ",TRAITS.TARGET.RANK[target_of_interest],"-stratum.\n"))
    fit <- survfit(as.formula(paste0("y ~ ", TRAITS.TARGET.RANK[target_of_interest])), data = TEMP.DF)
    
    cat(paste0("\n   > make a Kaplan-Meier-shizzle...\n"))
    # make Kaplan-Meier curve and save it
    show(ggsurvplot(fit, data = TEMP.DF,
                    palette = c("#DB003F", "#1290D9"),
                    # palete = c("F59D10", "#DB003F", "#49A01D", "#1290D9"),
                    linetype = c(1,2),
                    ylim = c(0.75, 1),
                    # linetype = c(1,2,3,4),
                    # conf.int = FALSE, conf.int.fill = "#595A5C", conf.int.alpha = 0.1,
                    pval = FALSE, pval.method = FALSE, pval.size = 4,
                    risk.table = TRUE, risk.table.y.text = FALSE, tables.y.text.col = TRUE, fontsize = 4,
                    censor = FALSE,
                    legend = "right",
                    legend.title = paste0("",TRAITS.TARGET.RANK[target_of_interest],""),
                    legend.labs = c("low", "high"),
                    title = paste0("Risk of ",ep,""), xlab = "Time [days]", font.main = c(16, "bold", "black")))
    dev.copy2pdf(file = paste0(COX_loc,"/",
                               Today,".AERNASE.clin.hdac9.survival.",ep,".2G.",
                               TRAITS.TARGET.RANK[target_of_interest],".30days.pdf"), width = 12, height = 10, onefile = FALSE)

    cat(paste0("\n   > perform the Cox-regression fashizzle and plot it...\n"))
    ### Do Cox-regression and plot it
    
    ### MODEL 2 adjusted for age, sex, hypertension, diabetes, smoking, LDL-C levels, lipid-lowering drugs, antiplatelet drugs, eGFR, BMI, history of CVD, level of stenosis
    cox = coxph(Surv(TEMP.DF[,eptime], event) ~ TEMP.DF[[ TRAITS.TARGET.RANK[target_of_interest] ]]+Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus + SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + MedHx_CVD + stenose, data = TEMP.DF)
    coxplot = coxph(Surv(TEMP.DF[,eptime], event) ~ strata(TEMP.DF[[ TRAITS.TARGET.RANK[target_of_interest] ]])+Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus + SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + MedHx_CVD + stenose, data = TEMP.DF)

  
    plot(survfit(coxplot), main = paste0("Cox proportional hazard of [",ep,"] per [",eptime,"]."),
         ylim = c(0.75, 1), xlim = c(0,3), col = c("#DB003F", "#1290D9"),
         # ylim = c(0, 1), xlim = c(0,3), col = c("#DB003F", "#1290D9"),
         lty = c(1,2), lwd = 2,
         ylab = "Suvival probability", xlab = "FU time [days]",
         mark.time = FALSE, axes = FALSE, bty = "n")
    legend("topright",
           c("low", "high"),
           title = paste0("",TRAITS.TARGET.RANK[target_of_interest],""),
           col = c("#DB003F", "#1290D9"),
           lty = c(1,2), lwd = 2,
           bty = "n")
    axis(side = 1, at = seq(0, 3, by = 1))
    axis(side = 2, at = seq(0, 1, by = 0.2))
    dev.copy2pdf(file = paste0(COX_loc,"/",
                               Today,".AERNASE.clin.hdac9.Cox.",ep,".2G.",
                               # Today,".AERNASE.clin.hdac9.Cox.",ep,".4G.",
                               TRAITS.TARGET.RANK[target_of_interest],".MODEL2.30days.pdf"), height = 12, width = 10, onefile = TRUE)

    show(summary(cox))

    cat(paste0("\n   > writing the Cox-regression fashizzle to Excel...\n"))

    COX.results.TEMP <- data.frame(matrix(NA, ncol = 12, nrow = 0))
    COX.results.TEMP[1,] = COX.STAT(cox, "AERNASE.clin.hdac9", ep, TRAITS.TARGET.RANK[target_of_interest])
    COX.results = rbind(COX.results, COX.results.TEMP)

  }
}

cat("- Edit the column names...\n")
colnames(COX.results) = c("Dataset", "Outcome", "CpG",
                          "Beta", "s.e.m.",
                          "HR", "low95CI", "up95CI",
                          "Z-value", "P-value", "SampleSize", "N_events")

cat("- Correct the variable types...\n")
COX.results$Beta <- as.numeric(COX.results$Beta)
COX.results$s.e.m. <- as.numeric(COX.results$s.e.m.)
COX.results$HR <- as.numeric(COX.results$HR)
COX.results$low95CI <- as.numeric(COX.results$low95CI)
COX.results$up95CI <- as.numeric(COX.results$up95CI)
COX.results$`Z-value` <- as.numeric(COX.results$`Z-value`)
COX.results$`P-value` <- as.numeric(COX.results$`P-value`)
COX.results$SampleSize <- as.numeric(COX.results$SampleSize)
COX.results$N_events <- as.numeric(COX.results$N_events)

AERNASE.clin.hdac9.COX.results <- COX.results

# Save the data
cat("- Writing results to Excel-file...\n")
head.style <- createStyle(textDecoration = "BOLD")

write.xlsx(AERNASE.clin.hdac9.COX.results,
           file = paste0(OUT_loc, "/",Today,".AERNASE.clin.hdac9.Cox.2G.MODEL2.30days.xlsx"),
           creator = "Sander W. van der Laan",
           sheetName = "Results", headerStyle = head.style,
           rowNames = FALSE, colNames = TRUE, overwrite = TRUE)

# Removing intermediates
cat("- Removing intermediate files...\n")
rm(TEMP.DF, target_of_interest, fit, cox, coxplot, COX.results, COX.results.TEMP, head.style, AERNASE.clin.hdac9.COX.results)

90-days follow-up

Model 1
# Set up a dataframe to receive results
COX.results <- data.frame(matrix(NA, ncol = 12, nrow = 0))

# Looping over each target_of_interest/endpoint/time combination
for (i in 1:length(times90)){
  eptime = times90[i]
  ep = endpoints90[i]
  cat(paste0("* Analyzing the effect of plaque target-of-interest on [",ep,"].\n"))
  cat(" - creating temporary SE for this work.\n")
  TEMP.DF = as.data.frame(AERNASE.clin.hdac9)
  cat(" - making a 'Surv' object and adding this to temporary dataframe.\n")
  TEMP.DF$event <- as.integer(TEMP.DF[,ep])
  TEMP.DF$y <- Surv(time = TEMP.DF[,eptime], event = TEMP.DF$event)
  cat(" - making strata of each of the plaque target-of-interest and start survival analysis.\n")
  
  for (target_of_interest in 1:length(TRAITS.TARGET.RANK)){
    cat(paste0("   > processing [",TRAITS.TARGET.RANK[target_of_interest],"]; ",target_of_interest," out of ",length(TRAITS.TARGET.RANK)," target-of-interest.\n"))
    # splitting into two groups
    TEMP.DF[[ TRAITS.TARGET.RANK[target_of_interest] ]] <- cut2(TEMP.DF[,TRAITS.TARGET.RANK[target_of_interest]], g = 2)
    cat(paste0("   > cross tabulation of ",TRAITS.TARGET.RANK[target_of_interest],"-stratum.\n"))
    show(table(TEMP.DF[[ TRAITS.TARGET.RANK[target_of_interest] ]]))
    
    cat(paste0("\n   > fitting the model for ",TRAITS.TARGET.RANK[target_of_interest],"-stratum.\n"))
    fit <- survfit(as.formula(paste0("y ~ ", TRAITS.TARGET.RANK[target_of_interest])), data = TEMP.DF)
    
    cat(paste0("\n   > make a Kaplan-Meier-shizzle...\n"))
    # make Kaplan-Meier curve and save it
    show(ggsurvplot(fit, data = TEMP.DF,
                    palette = c("#DB003F", "#1290D9"),
                    # palete = c("F59D10", "#DB003F", "#49A01D", "#1290D9"),
                    linetype = c(1,2),
                    ylim = c(0.75, 1),
                    # linetype = c(1,2,3,4),
                    # conf.int = FALSE, conf.int.fill = "#595A5C", conf.int.alpha = 0.1,
                    pval = FALSE, pval.method = FALSE, pval.size = 4,
                    risk.table = TRUE, risk.table.y.text = FALSE, tables.y.text.col = TRUE, fontsize = 4,
                    censor = FALSE,
                    legend = "right",
                    legend.title = paste0("",TRAITS.TARGET.RANK[target_of_interest],""),
                    legend.labs = c("low", "high"),
                    title = paste0("Risk of ",ep,""), xlab = "Time [days]", font.main = c(16, "bold", "black")))
    dev.copy2pdf(file = paste0(COX_loc,"/",
                               Today,".AERNASE.clin.hdac9.survival.",ep,".2G.",
                               TRAITS.TARGET.RANK[target_of_interest],".90days.pdf"), width = 12, height = 10, onefile = FALSE)

    cat(paste0("\n   > perform the Cox-regression fashizzle and plot it...\n"))
    ### Do Cox-regression and plot it
    
    ### MODEL 1 (Simple model)
    cox = coxph(Surv(TEMP.DF[,eptime], event) ~ TEMP.DF[[ TRAITS.TARGET.RANK[target_of_interest] ]]+Age+Gender + ORdate_year, data = TEMP.DF)
    coxplot = coxph(Surv(TEMP.DF[,eptime], event) ~ strata(TEMP.DF[[ TRAITS.TARGET.RANK[target_of_interest] ]])+Age+Gender + ORdate_year, data = TEMP.DF)

    plot(survfit(coxplot), main = paste0("Cox proportional hazard of [",ep,"] per [",eptime,"]."),
         ylim = c(0.75, 1), xlim = c(0,3), col = c("#595A5C", "#DB003F", "#1290D9"),
         # ylim = c(0, 1), xlim = c(0,3), col = c("#DB003F", "#1290D9"),
         lty = c(1,2), lwd = 2,
         ylab = "Suvival probability", xlab = "FU time [days]",
         mark.time = FALSE, axes = FALSE, bty = "n")
    legend("topright",
           c("low", "high"),
           title = paste0("",TRAITS.TARGET.RANK[target_of_interest],""),
           col = c("#DB003F", "#1290D9"),
           lty = c(1,2), lwd = 2,
           bty = "n")
    axis(side = 1, at = seq(0, 3, by = 1))
    axis(side = 2, at = seq(0, 1, by = 0.2))
    dev.copy2pdf(file = paste0(COX_loc,"/",
                               Today,".AERNASE.clin.hdac9.Cox.",ep,".2G.",
                               # Today,".AERNASE.clin.hdac9.Cox.",ep,".4G.",
                               TRAITS.TARGET.RANK[target_of_interest],".MODEL1.90days.pdf"), height = 12, width = 10, onefile = TRUE)
    show(summary(cox))

    cat(paste0("\n   > writing the Cox-regression fashizzle to Excel...\n"))

    COX.results.TEMP <- data.frame(matrix(NA, ncol = 12, nrow = 0))
    COX.results.TEMP[1,] = COX.STAT(cox, "AERNASE.clin.hdac9", ep, TRAITS.TARGET.RANK[target_of_interest])
    COX.results = rbind(COX.results, COX.results.TEMP)

  }
}

cat("- Edit the column names...\n")
colnames(COX.results) = c("Dataset", "Outcome", "CpG",
                          "Beta", "s.e.m.",
                          "HR", "low95CI", "up95CI",
                          "Z-value", "P-value", "SampleSize", "N_events")

cat("- Correct the variable types...\n")
COX.results$Beta <- as.numeric(COX.results$Beta)
COX.results$s.e.m. <- as.numeric(COX.results$s.e.m.)
COX.results$HR <- as.numeric(COX.results$HR)
COX.results$low95CI <- as.numeric(COX.results$low95CI)
COX.results$up95CI <- as.numeric(COX.results$up95CI)
COX.results$`Z-value` <- as.numeric(COX.results$`Z-value`)
COX.results$`P-value` <- as.numeric(COX.results$`P-value`)
COX.results$SampleSize <- as.numeric(COX.results$SampleSize)
COX.results$N_events <- as.numeric(COX.results$N_events)

AERNASE.clin.hdac9.COX.results <- COX.results

# Save the data
library(openxlsx)
cat("- Writing results to Excel-file...\n")
head.style <- createStyle(textDecoration = "BOLD")
write.xlsx(AERNASE.clin.hdac9.COX.results,
           file = paste0(OUT_loc, "/",Today,".AERNASE.clin.hdac9.Cox.2G.MODEL1.90days.xlsx"),
           creator = "Sander W. van der Laan",
           sheetName = "Results", headerStyle = head.style,
           rowNames = FALSE, colNames = TRUE, overwrite = TRUE)

# Removing intermediates
cat("- Removing intermediate files...\n")
rm(TEMP.DF, target_of_interest, fit, cox, coxplot, COX.results, COX.results.TEMP, head.style, AERNASE.clin.hdac9.COX.results)
Model 2
# Set up a dataframe to receive results
COX.results <- data.frame(matrix(NA, ncol = 12, nrow = 0))

# Looping over each target_of_interest/endpoint/time combination
for (i in 1:length(times90)){
  eptime = times90[i]
  ep = endpoints90[i]
  cat(paste0("* Analyzing the effect of plaque target-of-interest on [",ep,"].\n"))
  cat(" - creating temporary SE for this work.\n")
  TEMP.DF = as.data.frame(AERNASE.clin.hdac9)
  cat(" - making a 'Surv' object and adding this to temporary dataframe.\n")
  TEMP.DF$event <- as.integer(TEMP.DF[,ep])
  #as.integer(TEMP.DF[,ep] == "Excluded")

  TEMP.DF$y <- Surv(time = TEMP.DF[,eptime], event = TEMP.DF$event)
  cat(" - making strata of each of the plaque target-of-interest and start survival analysis.\n")
  
  for (target_of_interest in 1:length(TRAITS.TARGET.RANK)){
    cat(paste0("   > processing [",TRAITS.TARGET.RANK[target_of_interest],"]; ",target_of_interest," out of ",length(TRAITS.TARGET.RANK)," target-of-interest.\n"))
    # splitting into two groups
    TEMP.DF[[ TRAITS.TARGET.RANK[target_of_interest] ]] <- cut2(TEMP.DF[,TRAITS.TARGET.RANK[target_of_interest]], g = 2)
    cat(paste0("   > cross tabulation of ",TRAITS.TARGET.RANK[target_of_interest],"-stratum.\n"))
    show(table(TEMP.DF[[ TRAITS.TARGET.RANK[target_of_interest] ]]))
    
    cat(paste0("\n   > fitting the model for ",TRAITS.TARGET.RANK[target_of_interest],"-stratum.\n"))
    fit <- survfit(as.formula(paste0("y ~ ", TRAITS.TARGET.RANK[target_of_interest])), data = TEMP.DF)
    
    cat(paste0("\n   > make a Kaplan-Meier-shizzle...\n"))
    # make Kaplan-Meier curve and save it
    show(ggsurvplot(fit, data = TEMP.DF,
                    palette = c("#DB003F", "#1290D9"),
                    # palete = c("F59D10", "#DB003F", "#49A01D", "#1290D9"),
                    linetype = c(1,2),
                    ylim = c(0.75, 1),
                    # linetype = c(1,2,3,4),
                    # conf.int = FALSE, conf.int.fill = "#595A5C", conf.int.alpha = 0.1,
                    pval = FALSE, pval.method = FALSE, pval.size = 4,
                    risk.table = TRUE, risk.table.y.text = FALSE, tables.y.text.col = TRUE, fontsize = 4,
                    censor = FALSE,
                    legend = "right",
                    legend.title = paste0("",TRAITS.TARGET.RANK[target_of_interest],""),
                    legend.labs = c("low", "high"),
                    title = paste0("Risk of ",ep,""), xlab = "Time [days]", font.main = c(16, "bold", "black")))
    dev.copy2pdf(file = paste0(COX_loc,"/",
                               Today,".AERNASE.clin.hdac9.survival.",ep,".2G.",
                               TRAITS.TARGET.RANK[target_of_interest],".90days.pdf"), width = 12, height = 10, onefile = FALSE)

    cat(paste0("\n   > perform the Cox-regression fashizzle and plot it...\n"))
    ### Do Cox-regression and plot it
    
    ### MODEL 2 adjusted for age, sex, hypertension, diabetes, smoking, LDL-C levels, lipid-lowering drugs, antiplatelet drugs, eGFR, BMI, history of CVD, level of stenosis
    cox = coxph(Surv(TEMP.DF[,eptime], event) ~ TEMP.DF[[ TRAITS.TARGET.RANK[target_of_interest] ]]+Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus + SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + MedHx_CVD + stenose, data = TEMP.DF)
    coxplot = coxph(Surv(TEMP.DF[,eptime], event) ~ strata(TEMP.DF[[ TRAITS.TARGET.RANK[target_of_interest] ]])+Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus + SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + MedHx_CVD + stenose, data = TEMP.DF)

  
    plot(survfit(coxplot), main = paste0("Cox proportional hazard of [",ep,"] per [",eptime,"]."),
         ylim = c(0.75, 1), xlim = c(0,3), col = c("#DB003F", "#1290D9"),
         # ylim = c(0, 1), xlim = c(0,3), col = c("#DB003F", "#1290D9"),
         lty = c(1,2), lwd = 2,
         ylab = "Suvival probability", xlab = "FU time [days]",
         mark.time = FALSE, axes = FALSE, bty = "n")
    legend("topright",
           c("low", "high"),
           title = paste0("",TRAITS.TARGET.RANK[target_of_interest],""),
           col = c("#DB003F", "#1290D9"),
           lty = c(1,2), lwd = 2,
           bty = "n")
    axis(side = 1, at = seq(0, 3, by = 1))
    axis(side = 2, at = seq(0, 1, by = 0.2))
    dev.copy2pdf(file = paste0(COX_loc,"/",
                               Today,".AERNASE.clin.hdac9.Cox.",ep,".2G.",
                               # Today,".AERNASE.clin.hdac9.Cox.",ep,".4G.",
                               TRAITS.TARGET.RANK[target_of_interest],".MODEL2.90days.pdf"), height = 12, width = 10, onefile = TRUE)

    show(summary(cox))

    cat(paste0("\n   > writing the Cox-regression fashizzle to Excel...\n"))

    COX.results.TEMP <- data.frame(matrix(NA, ncol = 12, nrow = 0))
    COX.results.TEMP[1,] = COX.STAT(cox, "AERNASE.clin.hdac9", ep, TRAITS.TARGET.RANK[target_of_interest])
    COX.results = rbind(COX.results, COX.results.TEMP)

  }
}

cat("- Edit the column names...\n")
colnames(COX.results) = c("Dataset", "Outcome", "CpG",
                          "Beta", "s.e.m.",
                          "HR", "low95CI", "up95CI",
                          "Z-value", "P-value", "SampleSize", "N_events")

cat("- Correct the variable types...\n")
COX.results$Beta <- as.numeric(COX.results$Beta)
COX.results$s.e.m. <- as.numeric(COX.results$s.e.m.)
COX.results$HR <- as.numeric(COX.results$HR)
COX.results$low95CI <- as.numeric(COX.results$low95CI)
COX.results$up95CI <- as.numeric(COX.results$up95CI)
COX.results$`Z-value` <- as.numeric(COX.results$`Z-value`)
COX.results$`P-value` <- as.numeric(COX.results$`P-value`)
COX.results$SampleSize <- as.numeric(COX.results$SampleSize)
COX.results$N_events <- as.numeric(COX.results$N_events)

AERNASE.clin.hdac9.COX.results <- COX.results

# Save the data
cat("- Writing results to Excel-file...\n")
head.style <- createStyle(textDecoration = "BOLD")
write.xlsx(AERNASE.clin.hdac9.COX.results,
           file = paste0(OUT_loc, "/",Today,".AERNASE.clin.hdac9.Cox.2G.MODEL2.90days.xlsx"),
           creator = "Sander W. van der Laan",
           sheetName = "Results", headerStyle = head.style,
           rowNames = FALSE, colNames = TRUE, overwrite = TRUE)

# Removing intermediates
cat("- Removing intermediate files...\n")
rm(TEMP.DF, target_of_interest, fit, cox, coxplot, COX.results, COX.results.TEMP, head.style, AERNASE.clin.hdac9.COX.results)

Correlations

We correlated plaque levels of the biomarkers.

Plaque HDAC9 expression levels


# Installation of ggcorrplot()
# --------------------------------
# if(!require(devtools)) 
#   install.packages("devtools")
# devtools::install_github("kassambara/ggcorrplot")

library(ggcorrplot)

# Creating matrix - inverse-rank transformation
# --------------------------------
# AERNASE.clin.hdac9.temp <- subset(AERNASE.clin.hdac9, 
#                           select = c("IL6_rank", "MCP1_rank", "IL6_pg_ug_2015_rank", "MCP1_pg_ug_2015_rank", "IL6R_pg_ug_2015_rank",
#                                                TRAITS.BIN, TRAITS.CON.RANK)
#                                     )
# AERNASE.clin.hdac9.temp <- subset(AERNASE.clin.hdac9, 
#                           select = c("MCP1_rank", "MCP1_pg_ug_2015_rank",
#                                                TRAITS.BIN, TRAITS.CON.RANK)
#                                     )
AERNASE.clin.hdac9.temp <- subset(AERNASE.clin.hdac9, 
                          select = c("HDAC9",
                                     TRAITS.BIN, 
                                     TRAITS.CON.RANK,
                                     "Symptoms.5G", "AsymptSympt", "EP_major", "EP_composite")
                                    )


AERNASE.clin.hdac9.temp$CalcificationPlaque <- as.numeric(AERNASE.clin.hdac9.temp$CalcificationPlaque)
AERNASE.clin.hdac9.temp$CollagenPlaque <- as.numeric(AERNASE.clin.hdac9.temp$CollagenPlaque)
AERNASE.clin.hdac9.temp$Fat10Perc <- as.numeric(AERNASE.clin.hdac9.temp$Fat10Perc)
AERNASE.clin.hdac9.temp$MAC_binned <- as.numeric(AERNASE.clin.hdac9.temp$MAC_binned)
AERNASE.clin.hdac9.temp$SMC_binned <- as.numeric(AERNASE.clin.hdac9.temp$SMC_binned)
AERNASE.clin.hdac9.temp$IPH <- as.numeric(AERNASE.clin.hdac9.temp$IPH)
AERNASE.clin.hdac9.temp$Symptoms.5G <- as.numeric(AERNASE.clin.hdac9.temp$Symptoms.5G)
AERNASE.clin.hdac9.temp$AsymptSympt <- as.numeric(AERNASE.clin.hdac9.temp$AsymptSympt)
AERNASE.clin.hdac9.temp$EP_major <- as.numeric(AERNASE.clin.hdac9.temp$EP_major)
AERNASE.clin.hdac9.temp$EP_composite <- as.numeric(AERNASE.clin.hdac9.temp$EP_composite)
str(AERNASE.clin.hdac9.temp)
'data.frame':   623 obs. of  15 variables:
 $ HDAC9                 : int  24 11 7 5 13 46 14 0 30 12 ...
 $ CalcificationPlaque   : num  2 2 2 1 1 1 2 2 2 1 ...
 $ CollagenPlaque        : num  2 2 2 2 2 2 1 2 2 2 ...
 $ Fat10Perc             : num  2 2 2 2 1 1 2 1 2 1 ...
 $ IPH                   : num  1 2 2 1 1 1 1 2 1 1 ...
 $ MAC_binned            : num  2 1 2 1 1 2 2 1 2 1 ...
 $ SMC_binned            : num  2 2 2 2 2 2 1 2 2 2 ...
 $ MAC_rankNorm          : num  -0.0695 -0.7504 0.9099 -0.3812 -1.0959 ...
 $ SMC_rankNorm          : num  1.526 0.58 0.674 1.26 0.341 ...
 $ MAC_SMC_ratio_rank    : num  -0.215 -0.888 0.722 -0.366 -1.331 ...
 $ VesselDensity_rankNorm: num  -0.723 0.769 0.854 1.14 -0.861 ...
 $ Symptoms.5G           : num  6 6 5 6 2 5 1 1 1 1 ...
 $ AsymptSympt           : num  3 3 3 3 2 3 1 1 1 1 ...
 $ EP_major              : num  2 2 2 2 3 3 3 2 2 2 ...
 $ EP_composite          : num  2 3 3 2 3 3 3 2 2 2 ...
AERNASE.clin.hdac9.matrix.RANK <- as.matrix(AERNASE.clin.hdac9.temp)
rm(AERNASE.clin.hdac9.temp)
corr_biomarkers.rank <- round(cor(AERNASE.clin.hdac9.matrix.RANK, 
                             use = "pairwise.complete.obs", #the correlation or covariance between each pair of variables is computed using all complete pairs of observations on those variables
                             method = "spearman"), 3)
corr_biomarkers.rank
                        HDAC9 CalcificationPlaque CollagenPlaque Fat10Perc    IPH MAC_binned SMC_binned MAC_rankNorm SMC_rankNorm MAC_SMC_ratio_rank
HDAC9                   1.000               0.045         -0.069     0.109  0.055      0.045     -0.091        0.000       -0.140             -0.084
CalcificationPlaque     0.045               1.000          0.126     0.029  0.083     -0.079      0.068       -0.006        0.015              0.041
CollagenPlaque         -0.069               0.126          1.000    -0.077  0.006      0.008      0.334        0.015        0.303              0.091
Fat10Perc               0.109               0.029         -0.077     1.000  0.201      0.293     -0.154        0.197       -0.250             -0.012
IPH                     0.055               0.083          0.006     0.201  1.000      0.088     -0.120        0.128       -0.080             -0.033
MAC_binned              0.045              -0.079          0.008     0.293  0.088      1.000      0.042        0.627       -0.041             -0.046
SMC_binned             -0.091               0.068          0.334    -0.154 -0.120      0.042      1.000        0.112        0.557              0.089
MAC_rankNorm            0.000              -0.006          0.015     0.197  0.128      0.627      0.112        1.000        0.212             -0.015
SMC_rankNorm           -0.140               0.015          0.303    -0.250 -0.080     -0.041      0.557        0.212        1.000              0.182
MAC_SMC_ratio_rank     -0.084               0.041          0.091    -0.012 -0.033     -0.046      0.089       -0.015        0.182              1.000
VesselDensity_rankNorm -0.034              -0.050          0.171     0.014  0.203      0.073      0.109        0.085        0.077              0.026
Symptoms.5G             0.069              -0.098         -0.107     0.139  0.022      0.065     -0.126        0.039       -0.172             -0.002
AsymptSympt             0.074              -0.113         -0.101     0.144  0.031      0.067     -0.172        0.023       -0.186             -0.007
EP_major               -0.058               0.062          0.047    -0.017  0.004     -0.041      0.078       -0.003        0.034              0.010
EP_composite           -0.008               0.066          0.049    -0.036  0.075     -0.063      0.031       -0.005        0.042              0.047
                       VesselDensity_rankNorm Symptoms.5G AsymptSympt EP_major EP_composite
HDAC9                                  -0.034       0.069       0.074   -0.058       -0.008
CalcificationPlaque                    -0.050      -0.098      -0.113    0.062        0.066
CollagenPlaque                          0.171      -0.107      -0.101    0.047        0.049
Fat10Perc                               0.014       0.139       0.144   -0.017       -0.036
IPH                                     0.203       0.022       0.031    0.004        0.075
MAC_binned                              0.073       0.065       0.067   -0.041       -0.063
SMC_binned                              0.109      -0.126      -0.172    0.078        0.031
MAC_rankNorm                            0.085       0.039       0.023   -0.003       -0.005
SMC_rankNorm                            0.077      -0.172      -0.186    0.034        0.042
MAC_SMC_ratio_rank                      0.026      -0.002      -0.007    0.010        0.047
VesselDensity_rankNorm                  1.000      -0.042      -0.042    0.111        0.167
Symptoms.5G                            -0.042       1.000       0.885    0.004       -0.047
AsymptSympt                            -0.042       0.885       1.000    0.002       -0.051
EP_major                                0.111       0.004       0.002    1.000        0.675
EP_composite                            0.167      -0.047      -0.051    0.675        1.000
corr_biomarkers_p.rank <- ggcorrplot::cor_pmat(AERNASE.clin.hdac9.matrix.RANK, use = "pairwise.complete.obs", method = "spearman", exact = FALSE)
# Add correlation coefficients
# --------------------------------
# argument lab = TRUE
ggcorrplot(corr_biomarkers.rank, 
           method = "square", 
           type = "lower",
           title = "Cross biomarker correlations", 
           show.legend = TRUE, legend.title = bquote("Spearman's"~italic(rho)),
           ggtheme = ggplot2::theme_minimal, outline.color = "#FFFFFF",
           show.diag = TRUE,
           hc.order = FALSE, 
           lab = FALSE,
           digits = 3,
           # p.mat = corr_biomarkers_p.rank, sig.level = 0.05,
           colors = c("#1290D9", "#FFFFFF", "#E55738"))

library(data.table)
# flattenCorrMatrix
# --------------------------------
# cormat : matrix of the correlation coefficients
# pmat : matrix of the correlation p-values
flattenCorrMatrix <- function(cormat, pmat) {
  ut <- upper.tri(cormat)
  data.frame(
    biomarker_row = rownames(cormat)[row(cormat)[ut]],
    biomarker_column = rownames(cormat)[col(cormat)[ut]],
    spearman_cor  =(cormat)[ut],
    pval = pmat[ut]
    )
}

corr_biomarkers.rank.df <- as.data.table(flattenCorrMatrix(corr_biomarkers.rank, corr_biomarkers_p.rank))
DT::datatable(corr_biomarkers.rank.df)
NA
# chart of a correlation matrix
# --------------------------------
# Alternative solution https://www.r-graph-gallery.com/199-correlation-matrix-with-ggally.html
install.packages.auto("PerformanceAnalytics")
chart.Correlation.new <- function (R, histogram = TRUE, method = c("pearson", "kendall", 
    "spearman"), ...) 
{
    x = checkData(R, method = "matrix")
    if (missing(method)) 
        method = method[1]
    cormeth <- method
    panel.cor <- function(x, y, digits = 2, prefix = "", use = "pairwise.complete.obs", 
        method = cormeth, cex.cor, ...) {
        usr <- par("usr")
        on.exit(par(usr))
        par(usr = c(0, 1, 0, 1))
        r <- cor(x, y, use = use, method = method)
        txt <- format(c(r, 0.123456789), digits = digits)[1]
        txt <- paste(prefix, txt, sep = "")
        if (missing(cex.cor)) 
            cex <- 0.8/strwidth(txt)
        test <- cor.test(as.numeric(x), as.numeric(y), method = method)
        Signif <- symnum(test$p.value, corr = FALSE, na = FALSE, 
            cutpoints = c(0, 0.001, 0.01, 0.05, 0.1, 1), symbols = c("***", 
                "**", "*", ".", " "))
        text(0.5, 0.5, txt, cex = cex * (abs(r) + 0.3)/1.3)
        text(0.8, 0.8, Signif, cex = cex, col = 2)
    }
    f <- function(t) {
        dnorm(t, mean = mean(x), sd = sd.xts(x))
    }
    dotargs <- list(...)
    dotargs$method <- NULL
    rm(method)
    hist.panel = function(x, ... = NULL) {
        par(new = TRUE)
        hist(x, col = "#1290D9", probability = TRUE, axes = FALSE, 
        # hist(x, col = "light gray", probability = TRUE, axes = FALSE, 
            main = "", breaks = "FD")
        lines(density(x, na.rm = TRUE), col = "#E55738", lwd = 1)
        rug(x)
    }
    if (histogram) 
        pairs(x, gap = 0, lower.panel = panel.smooth, upper.panel = panel.cor, 
            diag.panel = hist.panel, ...)
    else pairs(x, gap = 0, lower.panel = panel.smooth, upper.panel = panel.cor, ...)
}


chart.Correlation.new(AERNASE.clin.hdac9.matrix.RANK, method = "spearman", histogram = TRUE, pch = 3)

# alternative chart of a correlation matrix
# --------------------------------
# Alternative solution https://www.r-graph-gallery.com/199-correlation-matrix-with-ggally.html
install.packages.auto("GGally")

# Quick display of two cabapilities of GGally, to assess the distribution and correlation of variables 
library(GGally)
 
# From the help page:

ggpairs(AERNASE.clin.hdac9,
        columns = c("HDAC9", TRAITS.BIN, TRAITS.CON.RANK, "Symptoms.5G", "AsymptSympt", "EP_major", "EP_composite"),
        columnLabels = c("HDAC9",
                         "Calcification", "Collagen", "Fat 10%", "IPH", "Macrophages (binned)", "SMC (binned)", "Macrophages", "SMC", "Macrophage/SMC", "Vessel density",
                         "Symptoms", "Symptoms (grouped)", "MACE", "Composite"),
        method = c("spearman"),
        # ggplot2::aes(colour = Gender),
        progress = FALSE)
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

Saving datasets

For the purpose of downstream analyses we save the AEDB.CEA object, and the HDAC9 objects.


saveRDS(AEDB.CEA, file = paste0(OUT_loc, "/", Today, ".",TRAIT_OF_INTEREST,".AEDB.CEA.withupdates.RDS"))
saveRDS(AEDB.CEA, file = paste0(OUT_loc, "/", Today, ".",TRAIT_OF_INTEREST,".AERNASE.clin.hdac9.df.RDS"))
saveRDS(AEDB.CEA, file = paste0(OUT_loc, "/", Today, ".",TRAIT_OF_INTEREST,".AERNASE.hdac9.se.RDS"))
saveRDS(AEDB.CEA, file = paste0(OUT_loc, "/", Today, ".",TRAIT_OF_INTEREST,".AERNASE.hdac9.genedata.RDS"))

Session information


Version:      v1.0.1
Last update:  2021-03-19
Written by:   Sander W. van der Laan (s.w.vanderlaan-2[at]umcutrecht.nl).
Description:  Script to analyse HDAC9 from the Ather-Express Biobank Study.
Minimum requirements: R version 3.5.2 (2018-12-20) -- 'Eggshell Igloo', macOS Mojave (10.14.2).

**MoSCoW To-Do List**
The things we Must, Should, Could, and Would have given the time we have.
_M_

_S_

_C_

_W_

**Changes log**
* v1.0.1 Update to main AEDB (there is an error in the Age-variable in the new version). More patients in the bulk (623 vs 611 with the newer dataset).
* v1.0.0 Inital version.

sessionInfo()
R version 4.1.2 (2021-11-01)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS Monterey 12.2.1

Matrix products: default
LAPACK: /Library/Frameworks/R.framework/Versions/4.1/Resources/lib/libRlapack.dylib

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

attached base packages:
 [1] stats4    grid      tools     stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] RColorBrewer_1.1-2                      SeuratObject_4.0.4                      Seurat_4.0.6                           
 [4] PerformanceAnalytics_2.0.4              xts_0.12.1                              zoo_1.8-9                              
 [7] Hmisc_4.6-0                             Formula_1.2-4                           lattice_0.20-45                        
[10] survminer_0.4.9                         survival_3.3-1                          MASS_7.3-54                            
[13] ggcorrplot_0.1.3.999                    GGally_2.1.2                            annotables_0.1.91                      
[16] EnhancedVolcano_1.12.0                  ggrepel_0.9.1                           AnnotationFilter_1.18.0                
[19] TxDb.Hsapiens.UCSC.hg19.knownGene_3.2.2 mygene_1.30.0                           org.Hs.eg.db_3.14.0                    
[22] DESeq2_1.34.0                           SummarizedExperiment_1.24.0             MatrixGenerics_1.6.0                   
[25] matrixStats_0.61.0                      GenomicFeatures_1.46.3                  AnnotationDbi_1.56.2                   
[28] Biobase_2.54.0                          GenomicRanges_1.46.1                    GenomeInfoDb_1.30.0                    
[31] IRanges_2.28.0                          S4Vectors_0.32.3                        BiocGenerics_0.40.0                    
[34] patchwork_1.1.0.9000                    labelled_2.9.0                          openxlsx_4.2.5                         
[37] sjPlot_2.8.10                           UpSetR_1.4.0                            ggpubr_0.4.0                           
[40] forestplot_2.0.1                        checkmate_2.0.0                         magrittr_2.0.2                         
[43] pheatmap_1.0.12                         devtools_2.4.3                          usethis_2.1.5                          
[46] BlandAltmanLeh_0.3.1                    tableone_0.13.0                         haven_2.4.3                            
[49] eeptools_1.2.4                          DT_0.20                                 knitr_1.37                             
[52] forcats_0.5.1                           stringr_1.4.0                           purrr_0.3.4                            
[55] tibble_3.1.6                            ggplot2_3.3.5                           tidyverse_1.3.1                        
[58] data.table_1.14.2                       naniar_0.6.1                            tidyr_1.1.4                            
[61] dplyr_1.0.7                             optparse_1.7.1                          readr_2.1.1                            
[64] pander_0.6.4                            rmarkdown_2.11                          worcs_0.1.9.1                          

loaded via a namespace (and not attached):
  [1] mitools_2.4              pbapply_1.5-0            vctrs_0.3.8              mgcv_1.8-39              blob_1.2.2               spatstat.data_2.1-2     
  [7] later_1.3.0              nloptr_1.2.2.3           DBI_1.1.2                uwot_0.1.11              rappdirs_0.3.3           gsubfn_0.7              
 [13] jpeg_0.1-9               zlibbioc_1.40.0          sjmisc_2.8.9             htmlwidgets_1.5.4        mvtnorm_1.1-3            future_1.23.0           
 [19] leiden_0.3.9             parallel_4.1.2           irlba_2.3.5              markdown_1.1             Rcpp_1.0.8.3             KernSmooth_2.23-20      
 [25] promises_1.2.0.1         limma_3.50.0             DelayedArray_0.20.0      ggeffects_1.1.1          pkgload_1.2.4            fs_1.5.2                
 [31] textshaping_0.3.6        ranger_0.13.1            digest_0.6.29            png_0.1-7                sctransform_0.3.2        cowplot_1.1.1           
 [37] pkgconfig_2.0.3          ggbeeswarm_0.6.0         estimability_1.3         minqa_1.2.4              reticulate_1.22          beeswarm_0.4.0          
 [43] xfun_0.29                bslib_0.3.1              tidyselect_1.1.1         performance_0.8.0        reshape2_1.4.4           ica_1.0-2               
 [49] viridisLite_0.4.0        rtracklayer_1.54.0       pkgbuild_1.3.1           rlang_1.0.2              jquerylib_0.1.4          glue_1.6.2              
 [55] modelr_0.1.8             emmeans_1.7.2            ggsignif_0.6.3           bayestestR_0.11.5        labeling_0.4.2           maptools_1.1-2          
 [61] httpuv_1.6.5             class_7.3-20             Rttf2pt1_1.3.9           TH.data_1.1-0            annotate_1.72.0          jsonlite_1.7.2          
 [67] XVector_0.34.0           bit_4.0.4                mime_0.12                systemfonts_1.0.3        gridExtra_2.3            Rsamtools_2.10.0        
 [73] stringi_1.7.6            insight_0.16.0           processx_3.5.2           spatstat.sparse_2.1-0    scattermore_0.7          survey_4.1-1            
 [79] quadprog_1.5-8           bitops_1.0-7             cli_3.2.0                sqldf_0.4-11             maps_3.4.0               RSQLite_2.2.9           
 [85] prereg_0.5.0             rticles_0.22             rsconnect_0.8.25         rstudioapi_0.13          GenomicAlignments_1.30.0 nlme_3.1-155            
 [91] locfit_1.5-9.4           listenv_0.8.0            miniUI_0.1.1.1           survMisc_0.5.5           dbplyr_2.1.1             sessioninfo_1.2.2       
 [97] readxl_1.3.1             lifecycle_1.0.1          munsell_0.5.0            cellranger_1.1.0         ggsci_2.9                codetools_0.2-18        
[103] coda_0.19-4              vipor_0.4.5              lmtest_0.9-39            sys_3.4                  htmlTable_2.4.0          proto_1.0.0             
[109] xtable_1.8-4             ROCR_1.0-11              abind_1.4-5              farver_2.1.0             parallelly_1.30.0        km.ci_0.5-2             
[115] credentials_1.3.2        RANN_2.6.1               askpass_1.1              visdat_0.5.3             BiocIO_1.4.0             sjstats_0.18.1          
[121] goftest_1.2-3            RcppAnnoy_0.0.19         cluster_2.1.2            future.apply_1.8.1       extrafontdb_1.0          Matrix_1.4-0            
[127] ellipsis_0.3.2           prettyunits_1.1.1        lubridate_1.8.0          ggridges_0.5.3           reprex_2.0.1             igraph_1.2.11           
[133] sjlabelled_1.1.8         remotes_2.4.2            parameters_0.17.0        spatstat.utils_2.3-0     testthat_3.1.1           getopt_1.20.3           
[139] htmltools_0.5.2          BiocFileCache_2.2.0      yaml_2.2.1               utf8_1.2.2               plotly_4.10.0            XML_3.99-0.8            
[145] e1071_1.7-9              foreign_0.8-82           withr_2.5.0              fitdistrplus_1.1-6       BiocParallel_1.28.3      bit64_4.0.5             
[151] effectsize_0.6.0.1       multcomp_1.4-18          ProtGenerics_1.26.0      spatstat.core_2.3-2      Biostrings_2.62.0        ragg_1.2.1              
[157] memoise_2.0.1            evaluate_0.14            geneplotter_1.72.0       tzdb_0.2.0               extrafont_0.17           callr_3.7.0             
[163] ps_1.6.0                 curl_4.3.2               fansi_1.0.2              tensor_1.5               cachem_1.0.6             desc_1.4.0              
[169] deldir_1.0-6             proj4_1.0-10.1           rjson_0.2.21             rstatix_0.7.0            rprojroot_2.0.2          sass_0.4.0              
[175] sandwich_3.0-1           RCurl_1.98-1.5           proxy_0.4-26             car_3.0-12               xml2_1.3.3               httr_1.4.2              
[181] assertthat_0.2.1         boot_1.3-28              globals_0.14.0           R6_2.5.1                 nnet_7.3-17              progress_1.2.2          
[187] genefilter_1.76.0        KEGGREST_1.34.0          ggrastr_1.0.1            splines_4.1.2            carData_3.0-5            colorspace_2.0-3        
[193] generics_0.1.1           base64enc_0.1-3          chron_2.3-56             gridtext_0.1.4           gert_1.5.0               pillar_1.7.0            
[199] ggalt_0.4.0              sp_1.4-6                 GenomeInfoDbData_1.2.7   plyr_1.8.6               gtable_0.3.0             rvest_1.0.2             
[205] zip_2.2.0                restfulr_0.0.13          latticeExtra_0.6-29      biomaRt_2.50.2           fastmap_1.1.0            Cairo_1.5-14            
[211] crosstalk_1.2.0          datawizard_0.3.0         vcd_1.4-9                broom_0.7.11             openssl_1.4.6            scales_1.1.1            
[217] arm_1.12-2               filelock_1.0.2           backports_1.4.1          lme4_1.1-27.1            hms_1.1.1                Rtsne_0.15              
[223] shiny_1.7.1              KMsurv_0.1-5             ash_1.0-15               polyclip_1.10-0          lazyeval_0.2.2           crayon_1.5.0            
[229] reshape_0.8.8            rpart_4.1.16             spatstat.geom_2.3-1      compiler_4.1.2           ggtext_0.1.1            

Saving environment

save.image(paste0(PROJECT_loc, "/",Today,".",PROJECTNAME,".bulkRNAseq.main_analysis.RData"))
© 1979-2022 Sander W. van der Laan | s.w.vanderlaan[at]gmail.com | swvanderlaan.github.io.
---
title: "Main analysis"
author: "[Sander W. van der Laan, PhD](https://swvanderlaan.github.io) | @swvanderlaan | s.w.vanderlaan@gmail.com"
date: "`r Sys.Date()`"
output:
  html_notebook:
    cache: yes
    code_folding: hide
    collapse: yes
    df_print: paged
    fig.align: center
    fig_caption: yes
    fig_height: 6
    fig_retina: 2
    fig_width: 7
    highlight: tango
    theme: lumen
    toc: yes
    toc_float:
      collapsed: no
      smooth_scroll: yes
mainfont: Arial
subtitle: Accompanying 'Plaque expression levels of HDAC9 in association with plaque vulnerability traits and secondary vascular events in patients undergoing carotid endarterectomy, an analysis in the Athero-EXPRESS Biobank.'
editor_options:
  chunk_output_type: inline
# bibliography: references.bib
# knit: worcs::cite_all
---
# General Setup

```{r setup, include=FALSE}
# We recommend that you prepare your raw data for analysis in 'prepare_data.R',
# and end that file with either open_data(yourdata), or closed_data(yourdata).
# Then, uncomment the line below to load the original or synthetic data
# (whichever is available), to allow anyone to reproduce your code:
# load_data()

# further define some knitr-options.
knitr::opts_chunk$set(fig.width = 12, fig.height = 8, fig.path = 'Figures/', 
                      warning = TRUE, # show warnings during codebook generation
                      message = TRUE, # show messages during codebook generation
                      error = TRUE, # do not interrupt codebook generation in case of errors, 
                                    # usually better for debugging
                      echo = TRUE,  # show R code
                      eval = TRUE)

ggplot2::theme_set(ggplot2::theme_minimal())
# pander::panderOptions("table.split.table", Inf)
library("worcs")
library("rmarkdown")

```

```{r echo = FALSE}
rm(list = ls())
```

```{r LocalSystem, echo = FALSE}
### Operating System Version
### MacBook Pro
ROOT_loc = "/Users/swvanderlaan"

### MacBook Air 
# ROOT_loc = "/Users/slaan3"

### General
GENOMIC_loc = paste0(ROOT_loc, "/OneDrive - UMC Utrecht/Genomics")
AEDB_loc = paste0(GENOMIC_loc, "/Athero-Express/AE-AAA_GS_DBs")
LAB_loc = paste0(GENOMIC_loc, "/LabBusiness")

PROJECT_loc = paste0(ROOT_loc, "/git/CirculatoryHealth/AE_20211201_YAW_SWVANDERLAAN_HDAC9")

# Genetic and genomic data
STORAGE_loc = paste0(ROOT_loc, "/PLINK")
AERNA_loc = paste0(STORAGE_loc, "/_AE_ORIGINALS/AERNA")
AESCRNA_loc = paste0(STORAGE_loc, "/_AE_ORIGINALS/AESCRNA/prepped_data")
AEGSQC_loc = paste0(STORAGE_loc, "/_AE_ORIGINALS/AEGS_COMBINED_QC2018")

### SOME VARIABLES WE NEED DOWN THE LINE
TRAIT_OF_INTEREST = "HDAC9" # Phenotype
PROJECTNAME = "HDAC9"

cat("\nCreate a new analysis directory...\n")
ifelse(!dir.exists(file.path(PROJECT_loc, "/",PROJECTNAME)), 
       dir.create(file.path(PROJECT_loc, "/",PROJECTNAME)), 
       FALSE)
ANALYSIS_loc = paste0(PROJECT_loc,"/",PROJECTNAME)

ifelse(!dir.exists(file.path(ANALYSIS_loc, "/PLOTS")), 
       dir.create(file.path(ANALYSIS_loc, "/PLOTS")), 
       FALSE)
PLOT_loc = paste0(ANALYSIS_loc,"/PLOTS")

ifelse(!dir.exists(file.path(PLOT_loc, "/QC")), 
       dir.create(file.path(PLOT_loc, "/QC")), 
       FALSE)
QC_loc = paste0(PLOT_loc,"/QC")

ifelse(!dir.exists(file.path(ANALYSIS_loc, "/OUTPUT")), 
       dir.create(file.path(ANALYSIS_loc, "/OUTPUT")), 
       FALSE)
OUT_loc = paste0(ANALYSIS_loc, "/OUTPUT")

ifelse(!dir.exists(file.path(ANALYSIS_loc, "/BASELINE")), 
       dir.create(file.path(ANALYSIS_loc, "/BASELINE")), 
       FALSE)
BASELINE_loc = paste0(ANALYSIS_loc, "/BASELINE")

ifelse(!dir.exists(file.path(ANALYSIS_loc, "/COX")), 
       dir.create(file.path(ANALYSIS_loc, "/COX")), 
       FALSE)
COX_loc = paste0(ANALYSIS_loc, "/COX")



setwd(paste0(PROJECT_loc))
getwd()
list.files()

```

```{r Source functions}
source(paste0(PROJECT_loc, "/scripts/functions.R"))
```

```{r loading_packages, message=FALSE, warning=FALSE}
install.packages.auto("pander")
install.packages.auto("readr")
install.packages.auto("optparse")
install.packages.auto("tools")
install.packages.auto("dplyr")
install.packages.auto("tidyr")
install.packages.auto("naniar")

# To get 'data.table' with 'fwrite' to be able to directly write gzipped-files
# Ref: https://stackoverflow.com/questions/42788401/is-possible-to-use-fwrite-from-data-table-with-gzfile
# install.packages("data.table", repos = "https://Rdatatable.gitlab.io/data.table")
library(data.table)

install.packages.auto("tidyverse")
install.packages.auto("knitr")
install.packages.auto("DT")
install.packages.auto("eeptools")

install.packages.auto("openxlsx")

install.packages.auto("haven")
install.packages.auto("tableone")
install.packages.auto("sjPlot")

install.packages.auto("BlandAltmanLeh")

# Install the devtools package from Hadley Wickham
install.packages.auto('devtools')

# for plotting
install.packages.auto("pheatmap")
install.packages.auto("forestplot")
install.packages.auto("ggplot2")

install.packages.auto("ggpubr")

install.packages.auto("UpSetR")

devtools::install_github("thomasp85/patchwork")

install.packages.auto("GGally")
install.packages.auto("ggcorrplot")

# for Seurat etc
install.packages.auto("GenomicFeatures")
install.packages.auto("GenomicRanges")
install.packages.auto("SummarizedExperiment")
install.packages.auto("DESeq2")
install.packages.auto("org.Hs.eg.db")
install.packages.auto("mygene")
install.packages.auto("TxDb.Hsapiens.UCSC.hg19.knownGene")
install.packages.auto("org.Hs.eg.db")
install.packages.auto("AnnotationDbi")
install.packages.auto("EnsDb.Hsapiens.v86")
install.packages.auto("EnhancedVolcano")

```

```{r Setting: Colors}

Today = format(as.Date(as.POSIXlt(Sys.time())), "%Y%m%d")
Today.Report = format(as.Date(as.POSIXlt(Sys.time())), "%A, %B %d, %Y")

### UtrechtScienceParkColoursScheme
###
### WebsitetoconvertHEXtoRGB:http://hex.colorrrs.com.
### Forsomefunctionsyoushoulddividethesenumbersby255.
###
###	No.	Color			      HEX	(RGB)						              CHR		  MAF/INFO
###---------------------------------------------------------------------------------------
###	1	  yellow			    #FBB820 (251,184,32)				      =>	1		or 1.0>INFO
###	2	  gold			      #F59D10 (245,157,16)				      =>	2		
###	3	  salmon			    #E55738 (229,87,56)				      =>	3		or 0.05<MAF<0.2 or 0.4<INFO<0.6
###	4	  darkpink		    #DB003F ((219,0,63)				      =>	4		
###	5	  lightpink		    #E35493 (227,84,147)				      =>	5		or 0.8<INFO<1.0
###	6	  pink			      #D5267B (213,38,123)				      =>	6		
###	7	  hardpink		    #CC0071 (204,0,113)				      =>	7		
###	8	  lightpurple	    #A8448A (168,68,138)				      =>	8		
###	9	  purple			    #9A3480 (154,52,128)				      =>	9		
###	10	lavendel		    #8D5B9A (141,91,154)				      =>	10		
###	11	bluepurple		  #705296 (112,82,150)				      =>	11		
###	12	purpleblue		  #686AA9 (104,106,169)			      =>	12		
###	13	lightpurpleblue	#6173AD (97,115,173/101,120,180)	=>	13		
###	14	seablue			    #4C81BF (76,129,191)				      =>	14		
###	15	skyblue			    #2F8BC9 (47,139,201)				      =>	15		
###	16	azurblue		    #1290D9 (18,144,217)				      =>	16		or 0.01<MAF<0.05 or 0.2<INFO<0.4
###	17	lightazurblue	  #1396D8 (19,150,216)				      =>	17		
###	18	greenblue		    #15A6C1 (21,166,193)				      =>	18		
###	19	seaweedgreen	  #5EB17F (94,177,127)				      =>	19		
###	20	yellowgreen		  #86B833 (134,184,51)				      =>	20		
###	21	lightmossgreen	#C5D220 (197,210,32)				      =>	21		
###	22	mossgreen		    #9FC228 (159,194,40)				      =>	22		or MAF>0.20 or 0.6<INFO<0.8
###	23	lightgreen	  	#78B113 (120,177,19)				      =>	23/X
###	24	green			      #49A01D (73,160,29)				      =>	24/Y
###	25	grey			      #595A5C (89,90,92)				        =>	25/XY	or MAF<0.01 or 0.0<INFO<0.2
###	26	lightgrey		    #A2A3A4	(162,163,164)			      =>	26/MT
###
###	ADDITIONAL COLORS
###	27	midgrey			#D7D8D7
###	28	verylightgrey	#ECECEC"
###	29	white			#FFFFFF
###	30	black			#000000
###----------------------------------------------------------------------------------------------

uithof_color = c("#FBB820","#F59D10","#E55738","#DB003F","#E35493","#D5267B",
                 "#CC0071","#A8448A","#9A3480","#8D5B9A","#705296","#686AA9",
                 "#6173AD","#4C81BF","#2F8BC9","#1290D9","#1396D8","#15A6C1",
                 "#5EB17F","#86B833","#C5D220","#9FC228","#78B113","#49A01D",
                 "#595A5C","#A2A3A4", "#D7D8D7", "#ECECEC", "#FFFFFF", "#000000")

uithof_color_legend = c("#FBB820", "#F59D10", "#E55738", "#DB003F", "#E35493",
                        "#D5267B", "#CC0071", "#A8448A", "#9A3480", "#8D5B9A",
                        "#705296", "#686AA9", "#6173AD", "#4C81BF", "#2F8BC9",
                        "#1290D9", "#1396D8", "#15A6C1", "#5EB17F", "#86B833",
                        "#C5D220", "#9FC228", "#78B113", "#49A01D", "#595A5C",
                        "#A2A3A4", "#D7D8D7", "#ECECEC", "#FFFFFF", "#000000")
### ----------------------------------------------------------------------------
```

# Main analyses

Here we perform the main analyses. We first load the prepared RNAseq data, and extract only the relevant genes. 

## Targets

Here we obtain data from the `r TRAIT_OF_INTEREST` in plaques.

```{r targets, message=FALSE, warning=FALSE}
library(openxlsx)

gene_list_df <- read.xlsx(paste0(PROJECT_loc, "/targets/Genes.xlsx"), sheet = "Genes")

target_genes <- unlist(gene_list_df$Gene)
target_genes

```


# Loading data

We simply load the previously saved `RDS`-file and extract the clinical and RNAseq data from that.

```{r LoadAEDB}
AEDB.CEA <- readRDS(file = paste0(OUT_loc, "/20220319.",TRAIT_OF_INTEREST,".AEDB.CEA.RDS"))
AEDB.CEA$STUDY_NUMBER <- paste0("ae", AEDB.CEA$STUDY_NUMBER)
head(AEDB.CEA$STUDY_NUMBER)

AERNASE <- readRDS(file = paste0(OUT_loc, "/20220319.AERNA.CEA.623pts.SE.after_qc.IC_academic.RDS"))

```


```{r}
AERNASE.clin <- as_tibble(colData(AERNASE))

AERNASE.hdac9 <- subset(AERNASE, subset = (rowRanges(AERNASE)$symbol %in% target_genes))

row.names(AERNASE.hdac9) <- rowData(AERNASE.hdac9)$symbol
temp <- as_tibble(t(assay(AERNASE.hdac9)), rownames = "STUDY_NUMBER")

AERNASE.clin.hdac9 <- as.data.frame(merge(AERNASE.clin, temp, by.x = "STUDY_NUMBER", by.y = "STUDY_NUMBER", sort = TRUE))
rm(temp)

```

Getting some summary statistics.
```{r}
cat("Average expression of a random selection of 1000 genes.\n")
set.seed(141619)
mean(rowMeans(as_tibble(assay(AERNASE)) %>% dplyr::sample_n(1000)))

cat("\nAverage expression of target genes.\n")
rowMeans(assay(AERNASE.hdac9))

cat("\nGene information.\n")
rowRanges(AERNASE.hdac9)

AERNASE.hdac9.genedata <- as_tibble(rowRanges(AERNASE.hdac9))

AERNASE.hdac9.genedata
```


# Analyses

The analyses are focused on three elements: 

1) plaque vulnerability phenotypes
2) clinical status at inclusion (symptoms)
3) secondary clinical outcome during three (3) years of follow-up

## Covariates & other variables

1.  Age (continuous in 1-year increment). [`Age`]
2.  Sex (male vs. female). [`Gender`]
3.  Presence of hypertension at baseline (defined either as history of hypertension, SBP ≥140 mm Hg, DBP ≥90 mm Hg, or prescription of antihypertensive medications). [`Hypertension.composite`]
4.  Presence of diabetes mellitus at baseline (defined either as a history of diabetes and/or administration of glucose lowering medication). [`DiabetesStatus`]
5.  Smoking (current, ex-, never). [`SmokerStatus`]
6.  LDL-C levels (continuous). [`LDL_final`]
7.  Use of lipid-lowering drugs. [`Med.Statin.LLD`]
8.  Use of antiplatelet drugs. [`Med.all.antiplatelet`]
9.  eGFR (continuous). [`GFR_MDRD`]
10.	BMI (continuous). [`BMI`]
11.	History of cardiovascular disease (stroke, coronary artery disease, peripheral artery disease). [`MedHx_CVD`] combination of [`CAD_history`, `Stroke_history`, `Peripheral.interv`]
12.	Level of stenosis (50-70% vs. 70-99%). [`stenose`]
13. Year of surgery [`ORdate_year`] as we discovered in Van Lammeren _et al._ the composition of the plaque and therefore the Athero-Express Biobank Study has changed over the years. Likely through changes in lifestyle and primary prevention regimes.

## Models

We will analyze the data through four different models

- Model 1: adjusted for age, sex, and year of surgery
- Model 2: adjusted for age, sex, year of surgery, and additionally adjusted for history hypertension (defined from medical history and/or use of antihypertensive medications), diabetes (defined as history of a diagnosis and/or use of glucose-lowering medications), current smoking, LDL-C levels at time of operation, use of statins, use of antiplatelet agents, eGFR, BMI, history of cardiovascular disease (coronary artery disease, stroke, peripheral artery disease), and level of stenosis (50-70%, 70-90%, 90-99%)

## A. Cross-sectional analysis plaque phenotypes

In the cross-sectional analysis of plaque MCP1 levels we will focus on the following plaque vulnerability phenotypes:

- Percentage of macrophages (continuous trait)
- Percentage of SMCs (continuous trait)
- Number of intraplaque microvessels per 3-4 hotspots (continuous trait)
- Presence of moderate/heavy calcifications (binary trait)
- Presence of moderate/heavy collagen content (binary trait)
- Presence of lipid core no/<10% vs. >10% (binary trait)
- Presence of intraplaque hemorrhage (binary trait)

### Quantitative traits

We inspect the plaque characteristics, and `inverse-rank normal transformation` continuous phenotypes.

```{r CrossSec: plaques - transformations and visualisations continuous}

# macrophages
cat("Summary of data.\n")
summary(AERNASE.clin.hdac9$MAC_rankNorm)

min_macmean <- min(AERNASE.clin.hdac9$MAC_rankNorm, na.rm = TRUE)
cat(paste0("\nMinimum value % macrophages: ",min_macmean,".\n"))

ggpubr::gghistogram(AERNASE.clin.hdac9, "MAC_rankNorm", 
                    # y = "..count..", 
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"), 
                    add = "median", 
                    #add_density = TRUE,
                    rug = TRUE,
                    #add.params =  list(color = "black", linetype = 2), 
                    title = "% macrophages",
                    xlab = "inverse-rank normalized %", 
                    ggtheme = theme_minimal())

# smooth muscle cells
cat("Summary of data.\n")
summary(AERNASE.clin.hdac9$SMC_rankNorm)

min_smcmean <- min(AERNASE.clin.hdac9$SMC_rankNorm, na.rm = TRUE)
cat(paste0("\nMinimum value % smooth muscle cells: ",min_smcmean,".\n"))

ggpubr::gghistogram(AERNASE.clin.hdac9, "SMC_rankNorm", 
                    # y = "..count..", 
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"), 
                    add = "median", 
                    #add_density = TRUE,
                    rug = TRUE,
                    #add.params =  list(color = "black", linetype = 2), 
                    title = "% smooth muscle cells",
                    xlab = "inverse-rank normalized %", 
                    ggtheme = theme_minimal())

# vessel density
cat("Summary of data.\n")
summary(AERNASE.clin.hdac9$VesselDensity_rankNorm)

min_vesseldensity <- min(AERNASE.clin.hdac9$VesselDensity_rankNorm, na.rm = TRUE)
min_vesseldensity
cat(paste0("\nMinimum value number of intraplaque neovessels per 3-4 hotspots: ",min_vesseldensity,".\n"))

ggpubr::gghistogram(AERNASE.clin.hdac9, "VesselDensity_rankNorm", 
                    # y = "..count..", 
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"), 
                    add = "median", 
                    #add_density = TRUE,
                    rug = TRUE,
                    #add.params =  list(color = "black", linetype = 2), 
                    title = "number of intraplaque neovessels per 3-4 hotspots",
                   xlab = "inverse-rank normalized number", 
                    ggtheme = theme_minimal())
```

Given their strong correlation, we also introduce a macrophages/smooth muscle cell ratio. This is a proxy of the extend to which a plaque is inflammed ('unstable') as compared to 'stable'. 

```{r CrossSec: plaques - transformations and visualisations continuous MAC-SMC}

AERNASE.clin.hdac9$MAC_SMC_ratio <- AERNASE.clin.hdac9$MAC_rankNorm / AERNASE.clin.hdac9$SMC_rankNorm

AERNASE.clin.hdac9$MAC_SMC_ratio_rank  <- qnorm((rank(AERNASE.clin.hdac9$MAC_SMC_ratio, na.last = "keep") - 0.5) / sum(!is.na(AERNASE.clin.hdac9$MAC_SMC_ratio)))


cat("Summary of data.\n")
summary(AERNASE.clin.hdac9$MAC_rankNorm)
summary(AERNASE.clin.hdac9$SMC_rankNorm)
summary(AERNASE.clin.hdac9$MAC_SMC_ratio_rank)

ggpubr::gghistogram(AERNASE.clin.hdac9, "MAC_SMC_ratio_rank", 
                    # y = "..count..", 
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"), 
                    add = "median", 
                    #add_density = TRUE,
                    rug = TRUE,
                    #add.params =  list(color = "black", linetype = 2), 
                    title = "macrophages/smooth muscle cells ratio",
                    xlab = "inverse-rank normalized", 
                    ggtheme = theme_minimal())

```


### Binary traits
```{r CrossSec: plaques - transformations and visualisations binary}
library(dplyr)
# calcification
cat("Summary of data.\n")
summary(AERNASE.clin.hdac9$Calc.bin)
contrasts(AERNASE.clin.hdac9$Calc.bin)

AERNASE.clin.hdac9$CalcificationPlaque <- as.factor(AERNASE.clin.hdac9$Calc.bin)

df <- AERNASE.clin.hdac9 %>%
  dplyr::filter(!is.na(CalcificationPlaque)) %>%
  dplyr::group_by(Gender, CalcificationPlaque) %>%
dplyr::summarise(counts = n()) 

ggpubr::ggbarplot(df, x = "CalcificationPlaque", y = "counts",
                    # y = "..count..",
                    color = "white",
                    fill = "Gender",
                    palette = c("#DB003F", "#1290D9"),
                    label = TRUE, lab.vjust = 2, lab.col = "#FFFFFF",
                    title = "Calcification",
                    xlab = "calcification", 
                    ggtheme = theme_minimal())
rm(df)

# collagen
cat("Summary of data.\n")
summary(AERNASE.clin.hdac9$Collagen.bin)
contrasts(AERNASE.clin.hdac9$Collagen.bin)

AERNASE.clin.hdac9$CollagenPlaque <- as.factor(AERNASE.clin.hdac9$Collagen.bin)

df <- AERNASE.clin.hdac9 %>%
  dplyr::filter(!is.na(CollagenPlaque)) %>%
  dplyr::group_by(Gender, CollagenPlaque) %>%
dplyr::summarise(counts = n()) 

ggpubr::ggbarplot(df, x = "CollagenPlaque", y = "counts",
                    # y = "..count..",
                    color = "white",
                    fill = "Gender",
                    palette = c("#DB003F", "#1290D9"),
                    label = TRUE, lab.vjust = 2, lab.col = "#FFFFFF",
                    title = "Collagen",
                    xlab = "collagen", 
                    ggtheme = theme_minimal())
rm(df)

# fat 10%
cat("Summary of data.\n")
summary(AERNASE.clin.hdac9$Fat.bin_10)
contrasts(AERNASE.clin.hdac9$Fat.bin_10)

AERNASE.clin.hdac9$Fat10Perc <- as.factor(AERNASE.clin.hdac9$Fat.bin_10)

df <- AERNASE.clin.hdac9 %>%
  dplyr::filter(!is.na(Fat10Perc)) %>%
  dplyr::group_by(Gender, Fat10Perc) %>%
dplyr::summarise(counts = n()) 

ggpubr::ggbarplot(df, x = "Fat10Perc", y = "counts",
                    # y = "..count..",
                    color = "white",
                    fill = "Gender",
                    palette = c("#DB003F", "#1290D9"),
                    label = TRUE, lab.vjust = 2, lab.col = "#FFFFFF",
                    title = "Intraplaque fat",
                    xlab = "intraplaque fat", 
                    ggtheme = theme_minimal())
rm(df)

# macrophages binned
cat("Summary of data.\n")
summary(AERNASE.clin.hdac9$Macrophages.bin)
contrasts(AERNASE.clin.hdac9$Macrophages.bin)

AERNASE.clin.hdac9$MAC_binned <- as.factor(AERNASE.clin.hdac9$Macrophages.bin)

df <- AERNASE.clin.hdac9 %>%
  dplyr::filter(!is.na(MAC_binned)) %>%
  dplyr::group_by(Gender, MAC_binned) %>%
dplyr::summarise(counts = n()) 

ggpubr::ggbarplot(df, x = "MAC_binned", y = "counts",
                    # y = "..count..",
                    color = "white",
                    fill = "Gender",
                    palette = c("#DB003F", "#1290D9"),
                    label = TRUE, lab.vjust = 2, lab.col = "#FFFFFF",
                    title = "Macrophages (binned)",
                    xlab = "Macrophages", 
                    ggtheme = theme_minimal())
rm(df)



# SMC binned
cat("Summary of data.\n")
summary(AERNASE.clin.hdac9$SMC.bin)
contrasts(AERNASE.clin.hdac9$SMC.bin)

AERNASE.clin.hdac9$SMC_binned <- as.factor(AERNASE.clin.hdac9$SMC.bin)

df <- AERNASE.clin.hdac9 %>%
  dplyr::filter(!is.na(SMC_binned)) %>%
  dplyr::group_by(Gender, SMC_binned) %>%
dplyr::summarise(counts = n()) 

ggpubr::ggbarplot(df, x = "SMC_binned", y = "counts",
                    # y = "..count..",
                    color = "white",
                    fill = "Gender",
                    palette = c("#DB003F", "#1290D9"),
                    label = TRUE, lab.vjust = 2, lab.col = "#FFFFFF",
                    title = "SMC (binned)",
                    xlab = "SMC", 
                    ggtheme = theme_minimal())
rm(df)




# IPH
cat("Summary of data.\n")
summary(AERNASE.clin.hdac9$IPH.bin)
contrasts(AERNASE.clin.hdac9$IPH.bin)

AERNASE.clin.hdac9$IPH <- as.factor(AERNASE.clin.hdac9$IPH.bin)

df <- AERNASE.clin.hdac9 %>%
  dplyr::filter(!is.na(IPH)) %>%
  dplyr::group_by(Gender, IPH) %>%
dplyr::summarise(counts = n()) 

ggpubr::ggbarplot(df, x = "IPH", y = "counts",
                    # y = "..count..",
                    color = "white",
                    fill = "Gender",
                    palette = c("#DB003F", "#1290D9"),
                    label = TRUE, lab.vjust = 2, lab.col = "#FFFFFF",
                    title = "Intraplaque hemorrhage",
                    xlab = "intraplaque hemorrhage", 
                    ggtheme = theme_minimal())
rm(df)

# Symptoms
cat("Summary of data.\n")
summary(AERNASE.clin.hdac9$AsymptSympt)
contrasts(AERNASE.clin.hdac9$AsymptSympt)

AERNASE.clin.hdac9$AsymptSympt <- as.factor(AERNASE.clin.hdac9$AsymptSympt)

df <- AERNASE.clin.hdac9 %>%
  dplyr::filter(!is.na(AsymptSympt)) %>%
  dplyr::group_by(Gender, AsymptSympt) %>%
dplyr::summarise(counts = n()) 

ggpubr::ggbarplot(df, x = "AsymptSympt", y = "counts",
                    # y = "..count..",
                    color = "white",
                    fill = "Gender",
                    palette = c("#DB003F", "#1290D9"),
                    label = TRUE, lab.vjust = 2, lab.col = "#FFFFFF",
                    title = "Symptoms",
                    xlab = "symptoms", 
                    ggtheme = theme_minimal())
rm(df)

```

### Correlations between `r TRAIT_OF_INTEREST` plaque levels and surgery year

Here we compare the _`r TRAIT_OF_INTEREST`_ plaque gene expression levels.

```{r scatters: year of surgery}
p1 <- ggpubr::ggscatter(AERNASE.clin.hdac9, 
                        x = "ORyear", 
                        y = "HDAC9",
                        color = "#1290D9",
                        # fill = "Gender",
                        # palette = c("#1290D9", "#DB003F"),
                        add = "reg.line",
                        add.params =  list(color = "black", linetype = 2),
                        cor.coef = TRUE, cor.method = "spearman", 
                        xlab = "year of surgery",
                        ylab = "experiment 1",
                        title = "HDAC9 (normalized expression)",
                        ggtheme = theme_minimal())

p1 

rm(p1)

```



## Prepare modeling

In this section we make some variables to assist with analysis.

We fix the *diabetes* status variable.

```{r FixDiabetes, message=FALSE, warning=FALSE}

# Fix diabetes
attach(AEDB.CEA)
AEDB.CEA[,"DiabetesStatus"] <- NA
AEDB.CEA$DiabetesStatus[DM.composite == -999] <- NA
AEDB.CEA$DiabetesStatus[DM.composite == 0] <- "Control (no Diabetes Dx/Med)"
AEDB.CEA$DiabetesStatus[DM.composite == 1] <- "Diabetes"
detach(AEDB.CEA)

table(AEDB.CEA$DM.composite, AEDB.CEA$DiabetesStatus)
# AEDB.CEA.temp <- subset(AEDB.CEA,  select = c("STUDY_NUMBER", "UPID", "Age", "Gender", "Hospital", "Artery_summary", "DM.composite", "DiabetesStatus"))
# require(labelled)
# AEDB.CEA.temp$Gender <- to_factor(AEDB.CEA.temp$Gender)
# AEDB.CEA.temp$Hospital <- to_factor(AEDB.CEA.temp$Hospital)
# AEDB.CEA.temp$Artery_summary <- to_factor(AEDB.CEA.temp$Artery_summary)
# AEDB.CEA.temp$DiabetesStatus <- to_factor(AEDB.CEA.temp$DiabetesStatus)
# 
# DT::datatable(AEDB.CEA.temp[1:10,], caption = "Excerpt of the whole AEDB.CEA.", rownames = FALSE)
# 
# rm(AEDB.CEA.temp)

```

We will also fix a history of CAD, stroke or peripheral intervention status variable. This will be based on `CAD_history`, `Stroke_history`, and `Peripheral.interv`

```{r FixCAD_History, message=FALSE, warning=FALSE}
# AEDB.CEA$CAD_history
# AEDB.CEA$Stroke_history
# AEDB.CEA$Peripheral.interv

# Fix diabetes
attach(AEDB.CEA)
AEDB.CEA[,"MedHx_CVD"] <- NA
AEDB.CEA$MedHx_CVD[CAD_history == "No history CAD" | Stroke_history == 0 | Peripheral.interv == "no"] <- "No"
AEDB.CEA$MedHx_CVD[CAD_history == "History CAD" | Stroke_history == 1 | Peripheral.interv == "yes"] <- "yes"
detach(AEDB.CEA)

table(AEDB.CEA$CAD_history)
table(AEDB.CEA$Stroke_history)
table(AEDB.CEA$Peripheral.interv)
table(AEDB.CEA$MedHx_CVD)

# AEDB.CEA.temp <- subset(AEDB.CEA,  select = c("STUDY_NUMBER", "UPID", "Age", "Gender", "Hospital", "Artery_summary", "diet810", "AlcoholUse"))
# require(labelled)
# AEDB.CEA.temp$Gender <- to_factor(AEDB.CEA.temp$Gender)
# AEDB.CEA.temp$Hospital <- to_factor(AEDB.CEA.temp$Hospital)
# AEDB.CEA.temp$Artery_summary <- to_factor(AEDB.CEA.temp$Artery_summary)
# AEDB.CEA.temp$AlcoholUse <- to_factor(AEDB.CEA.temp$AlcoholUse)
# 
# DT::datatable(AEDB.CEA.temp[1:10,], caption = "Excerpt of the whole AEDB.CEA.", rownames = FALSE)
# 
# rm(AEDB.CEA.temp)


```


```{r}
AERNASE.clin.hdac9$DiabetesStatus <- NULL
AERNASE.clin.hdac9$MedHx_CVD <- NULL

AERNASE.clin.hdac9 <- merge(AERNASE.clin.hdac9, 
                            subset(AEDB.CEA, select = c("STUDY_NUMBER", "dateok", "DiabetesStatus", "MedHx_CVD")), 
                            by.x = "STUDY_NUMBER", by.y = "STUDY_NUMBER", sort = TRUE)

AERNASE.clin.hdac9$MedHx_CVD <- as_factor(AERNASE.clin.hdac9$MedHx_CVD)
AERNASE.clin.hdac9$DiabetesStatus <- as_factor(AERNASE.clin.hdac9$DiabetesStatus)

```


```{r CrossSec: plaques - setup regression }

study.samplesize = nrow(AERNASE.clin.hdac9) # study.samplesize is an expected variable in the GLM.BIN() GLM.CON() functions

TRAITS.TARGET.RANK = c("HDAC9")

TRAITS.CON.RANK = c("MAC_rankNorm", "SMC_rankNorm", "MAC_SMC_ratio_rank", "VesselDensity_rankNorm")

TRAITS.BIN = c("CalcificationPlaque", "CollagenPlaque", "Fat10Perc", "IPH",
               "MAC_binned", "SMC_binned")

# "Hospital", 
# "Age", "Gender", 
# "TC_final", "LDL_final", "HDL_final", "TG_final", 
# "systolic", "diastoli", "GFR_MDRD", "BMI", 
# "KDOQI", "BMI_WHO",
# "SmokerCurrent", "eCigarettes", "ePackYearsSmoking",
# "DiabetesStatus", "Hypertension.composite", 
# "Hypertension.drugs", "Med.anticoagulants", "Med.all.antiplatelet", "Med.Statin.LLD", 
# "Stroke_Dx", "sympt", "Symptoms.5G", "restenos",
# "EP_composite", "EP_composite_time",
# "macmean0", "smcmean0", "Macrophages.bin", "SMC.bin",
# "neutrophils", "Mast_cells_plaque",
# "IPH.bin", "vessel_density_averaged",
# "Calc.bin", "Collagen.bin", 
# "Fat.bin_10", "Fat.bin_40", "OverallPlaquePhenotype",
# "IL6_pg_ug_2015", "MCP1_pg_ug_2015", 
# "QC2018_FILTER", "CHIP", "SAMPLE_TYPE",
# "CAD_history", "Stroke_history", "Peripheral.interv",
# "stenose"

# 1.  Age (continuous in 1-year increment). [Age]
# 2.  Sex (male vs. female). [Gender]
# 3.  Presence of hypertension at baseline (defined either as history of hypertension, SBP ≥140 mm Hg, DBP ≥90 mm Hg, or prescription of antihypertensive medications). [Hypertension.composite]
# 4.  Presence of diabetes mellitus at baseline (defined either as a history of diabetes, administration of glucose lowering medication, HbA1c ≥6.5%, fasting glucose ≥126 mg/dl, .or random glucose levels ≥200 mg/dl). [DiabetesStatus]
# 5.  Smoking (current, ex-, never). [SmokerCurrent]
# 6.  LDL-C levels (continuous). [LDL_final]
# 7.  Use of lipid-lowering drugs. [Med.Statin.LLD]
# 8.  Use of antiplatelet drugs. [Med.all.antiplatelet]
# 9.  eGFR (continuous). [GFR_MDRD]
# 10.	BMI (continuous). [BMI]
# 11.	History of cardiovascular disease (stroke, coronary artery disease, peripheral artery disease). [MedHx_CVD] combinatino of: [CAD_history, Stroke_history, Peripheral.interv]
# 12.	Level of stenosis (50-70% vs. 70-99%). [stenose]

# Models 
# Model 1: adjusted for age and sex
# Model 2: adjusted for age, sex, hypertension, diabetes, smoking, LDL-C levels, lipid-lowering drugs, antiplatelet drugs, eGFR, BMI, history of CVD, level of stenosis,

AERNASE.clin.hdac9$ORdate_epoch <- as.numeric(AERNASE.clin.hdac9$dateok)
AERNASE.clin.hdac9$ORdate_year <- AERNASE.clin.hdac9$ORyear

cat("Summary of 'year of surgery' in 'epoch' (); coded as `numeric()`\n")
summary(AERNASE.clin.hdac9$ORdate_epoch)

cat("\nSummary of 'year of surgery' in 'years' (); coded as `factor()`\n")
table(AERNASE.clin.hdac9$ORdate_year)

# COVARIATES_M1 = c("Age", "Gender", "ORdate_year")
COVARIATES_M1 = c("Age", "Gender", "ORdate_epoch")

COVARIATES_M2 = c(COVARIATES_M1,  
               "Hypertension.composite", "DiabetesStatus", 
               "SmokerStatus", 
               # "SmokerCurrent",
               "Med.Statin.LLD", "Med.all.antiplatelet", 
               "GFR_MDRD", "BMI", 
               # "CAD_history", "Stroke_history", "Peripheral.interv", 
               "MedHx_CVD",
               "stenose")
str(AERNASE.clin.hdac9)
# COVARIATES_M3 = c(COVARIATES_M2, "LDL_final")

# COVARIATES_M4 = c(COVARIATES_M2, "hsCRP_plasma")

```

### Model 1

In this model we correct for _Age_, _Gender_, and _year of surgery_.

Here we use the inverse-rank normalized data - visually this is more normally distributed.

#### Quantitative plaque traits

```{r}
detach("package:EnsDb.Hsapiens.v86", unload = TRUE)
detach("package:ensembldb", unload = TRUE)

```

Analysis of continuous/quantitative plaque traits as a function of plaque `r TRAIT_OF_INTEREST` expression levels.
```{r CrossSec: plaques - linear regression MODEL1 RANK, paged.print=FALSE}
library(dplyr)
library(MASS)

GLM.results <- data.frame(matrix(NA, ncol = 15, nrow = 0))

cat("Running linear regression...\n")
for (target_of_interest in 1:length(TRAITS.TARGET.RANK)) {
  TARGET = TRAITS.TARGET.RANK[target_of_interest]
  cat(paste0("\nAnalysis of ",TARGET,".\n"))
  for (trait in 1:length(TRAITS.CON.RANK)) {
    TRAIT = TRAITS.CON.RANK[trait]
    cat(paste0("\n- processing ",TRAIT,"\n\n"))
    currentDF <- as.data.frame(AERNASE.clin.hdac9 %>%
      dplyr::select(., TARGET, TRAIT, COVARIATES_M1) %>%
      filter(complete.cases(.))) %>%
      filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
    # # for debug
    # print(DT::datatable(currentDF))
    # print(nrow(currentDF))
    # print(str(currentDF))
    ### univariate
    # fit <- lm(currentDF[,TARGET] ~ currentDF[,TRAIT] + Age + Gender + ORdate_year, data = currentDF)
    fit <- lm(currentDF[,TARGET] ~ currentDF[,TRAIT] + Age + Gender + ORdate_epoch, data = currentDF)
    model_step <- stepAIC(fit, direction = "both", trace = FALSE)
    print(model_step)
    print(summary(fit))

    GLM.results.TEMP <- data.frame(matrix(NA, ncol = 15, nrow = 0))
    GLM.results.TEMP[1,] = GLM.CON(fit, "AERNASE.clin.hdac9", TARGET, TRAIT, verbose = TRUE)
    GLM.results = rbind(GLM.results, GLM.results.TEMP)
  }
}
cat("Edit the column names...\n")
colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
                          "Beta", "s.e.m.",
                          "OR", "low95CI", "up95CI",
                          "T-value", "P-value", "r^2", "r^2_adj", "N", "Model_N", "Perc_Miss")

cat("Correct the variable types...\n")
GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`T-value` <- as.numeric(GLM.results$`T-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2` <- as.numeric(GLM.results$`r^2`)
GLM.results$`r^2_adj` <- as.numeric(GLM.results$`r^2_adj`)
GLM.results$`N` <- as.numeric(GLM.results$`N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)

# Save the data
cat("Writing results to Excel-file...\n")
### Univariate
library(openxlsx)
write.xlsx(GLM.results,
           file = paste0(OUT_loc, "/",Today,".AERNASE.clin.hdac9.Con.Uni.",TRAIT_OF_INTEREST,".PlaquePhenotypes.RANK.MODEL1.xlsx"),
           rowNames = FALSE, colNames = TRUE, sheetName = "Con.Uni.PlaquePheno")

# Removing intermediates
cat("Removing intermediate files...\n")
rm(TRAIT, trait, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)


```

#### Binary plaque traits

Analysis of binary plaque traits as a function of plaque `r TRAIT_OF_INTEREST` expression levels.
```{r CrossSec: plaques - logistic regression MODEL1 RANK, paged.print=FALSE}

GLM.results <- data.frame(matrix(NA, ncol = 16, nrow = 0))
for (target_of_interest in 1:length(TRAITS.TARGET.RANK)) {
  TARGET = TRAITS.TARGET.RANK[target_of_interest]
  cat(paste0("\nAnalysis of ",TARGET,".\n"))
  for (trait in 1:length(TRAITS.BIN)) {
    TRAIT = TRAITS.BIN[trait]
    cat(paste0("\n- processing ",TRAIT,"\n\n"))
    currentDF <- as.data.frame(AERNASE.clin.hdac9 %>%
      dplyr::select(., TARGET, TRAIT, COVARIATES_M1) %>%
      filter(complete.cases(.))) %>%
      filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
    # for debug
    # print(DT::datatable(currentDF))
    # print(nrow(currentDF))
    # print(str(currentDF))
    # print(class(currentDF[,TRAIT]))
    ### univariate
    # fit <- glm(as.factor(currentDF[,TRAIT]) ~ currentDF[,TARGET] + Age + Gender + ORdate_year,
    #           data  =  currentDF, family = binomial(link = "logit"))
    fit <- glm(as.factor(currentDF[,TRAIT]) ~ currentDF[,TARGET] + Age + Gender + ORdate_epoch,
              data  =  currentDF, family = binomial(link = "logit"))
    
    model_step <- stepAIC(fit, direction = "both", trace = FALSE)
    print(model_step)
    print(summary(fit))
    
    GLM.results.TEMP <- data.frame(matrix(NA, ncol = 16, nrow = 0))
    GLM.results.TEMP[1,] = GLM.BIN(fit, "AERNASE.clin.hdac9", TARGET, TRAIT, verbose = TRUE)
    GLM.results = rbind(GLM.results, GLM.results.TEMP)
  }
}
cat("Edit the column names...\n")
colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
                          "Beta", "s.e.m.",
                          "OR", "low95CI", "up95CI",
                          "Z-value", "P-value", "r^2_l", "r^2_cs", "r^2_nagelkerke", "N", "Model_N", "Perc_Miss")

cat("Correct the variable types...\n")
GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`Z-value` <- as.numeric(GLM.results$`Z-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2_l` <- as.numeric(GLM.results$`r^2_l`)
GLM.results$`r^2_cs` <- as.numeric(GLM.results$`r^2_cs`)
GLM.results$`r^2_nagelkerke` <- as.numeric(GLM.results$`r^2_nagelkerke`)
GLM.results$`N` <- as.numeric(GLM.results$`N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)

# Save the data
cat("Writing results to Excel-file...\n")

### Univariate
write.xlsx(GLM.results,
           file = paste0(OUT_loc, "/",Today,".AERNASE.clin.hdac9.Bin.Uni.",TRAIT_OF_INTEREST,".PlaquePhenotypes.RANK.MODEL1.xlsx"),
           rowNames = FALSE, colNames = TRUE, sheetName = "Bin.Uni.PlaquePheno")

# Removing intermediates
cat("Removing intermediate files...\n")
rm(TRAIT, trait, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)

```


### Model 2

In this model we correct for _Age_, _Gender_, _year of surgery_, _Hypertension status_, _Diabetes status_, _current smoker status_, _lipid-lowering drugs (LLDs)_, _antiplatelet medication_, _eGFR (MDRD)_, _BMI_, _MedHx_CVD_ (combination of _CAD history_, _stroke history_, and _peripheral interventions_), and _stenosis_.

Here we use the `inverse-rank normalized` data - visually this is more normally distributed.

#### Quantitative plaque traits

Analysis of continuous/quantitative plaque traits as a function of plaque `r TRAIT_OF_INTEREST` expression levels.
```{r CrossSec: plaques - linear regression MODEL2 RANK, paged.print=FALSE}

GLM.results <- data.frame(matrix(NA, ncol = 15, nrow = 0))
cat("Running linear regression...\n")
for (target_of_interest in 1:length(TRAITS.TARGET.RANK)) {
  TARGET = TRAITS.TARGET.RANK[target_of_interest]
  cat(paste0("\nAnalysis of ",TARGET,".\n"))
  for (trait in 1:length(TRAITS.CON.RANK)) {
    TRAIT = TRAITS.CON.RANK[trait]
    cat(paste0("\n- processing ",TRAIT,"\n\n"))
    currentDF <- as.data.frame(AERNASE.clin.hdac9 %>%
      dplyr::select(., TARGET, TRAIT, COVARIATES_M2) %>%
      filter(complete.cases(.))) %>%
      filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
    # for debug
    # print(DT::datatable(currentDF))
    # print(nrow(currentDF))
    # print(str(currentDF))
    ### univariate
    # fit <- lm(currentDF[,TARGET] ~ currentDF[,TRAIT] + Age + Gender + ORdate_year + 
    #             Hypertension.composite + DiabetesStatus + SmokerStatus + 
    #             Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
    #             MedHx_CVD + stenose, 
    #           data = currentDF)
    
    fit <- lm(currentDF[,TARGET] ~ currentDF[,TRAIT] + Age + Gender + ORdate_epoch +
              Hypertension.composite + DiabetesStatus + SmokerStatus + 
              Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
              MedHx_CVD + stenose, 
              data = currentDF) 
    
    model_step <- stepAIC(fit, direction = "both", trace = FALSE)
    print(model_step)
    print(summary(fit))
    
    GLM.results.TEMP <- data.frame(matrix(NA, ncol = 15, nrow = 0))
    GLM.results.TEMP[1,] = GLM.CON(fit, "AERNASE.clin.hdac9", TARGET, TRAIT, verbose = TRUE)
    GLM.results = rbind(GLM.results, GLM.results.TEMP)
  }
}
cat("Edit the column names...\n")
colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
                          "Beta", "s.e.m.",
                          "OR", "low95CI", "up95CI",
                          "T-value", "P-value", "r^2", "r^2_adj", "N", "Model_N", "Perc_Miss")

cat("Correct the variable types...\n")
GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`T-value` <- as.numeric(GLM.results$`T-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2` <- as.numeric(GLM.results$`r^2`)
GLM.results$`r^2_adj` <- as.numeric(GLM.results$`r^2_adj`)
GLM.results$`N` <- as.numeric(GLM.results$`N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)

# Save the data
cat("Writing results to Excel-file...\n")
### Univariate
library(openxlsx)
write.xlsx(GLM.results,
           file = paste0(OUT_loc, "/",Today,".AERNASE.clin.hdac9.Con.Multi.",TRAIT_OF_INTEREST,".PlaquePhenotypes.RANK.MODEL2.xlsx"),
           rowNames = FALSE, colNames = TRUE, sheetName = "Con.Multi.PlaquePheno")
# Removing intermediates
cat("Removing intermediate files...\n")
rm(TRAIT, trait, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)


```

#### Binary plaque traits

Analysis of binary plaque traits as a function of plaque MCP1 levels.
```{r CrossSec: plaques - logistic regression MODEL2 RANK, paged.print=FALSE}

GLM.results <- data.frame(matrix(NA, ncol = 16, nrow = 0))
for (target_of_interest in 1:length(TRAITS.TARGET.RANK)) {
  TARGET = TRAITS.TARGET.RANK[target_of_interest]
  cat(paste0("\nAnalysis of ",TARGET,".\n"))
  for (trait in 1:length(TRAITS.BIN)) {
    TRAIT = TRAITS.BIN[trait]
    cat(paste0("\n- processing ",TRAIT,"\n\n"))
    currentDF <- as.data.frame(AERNASE.clin.hdac9 %>%
      dplyr::select(., TARGET, TRAIT, COVARIATES_M2) %>%
      filter(complete.cases(.))) %>%
      filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
    # for debug
    # print(DT::datatable(currentDF))
    # print(nrow(currentDF))
    # print(str(currentDF))
    # print(class(currentDF[,TRAIT]))
    ### univariate
    # fit <- glm(as.factor(currentDF[,TRAIT]) ~ currentDF[,TARGET] + Age + Gender + ORdate_year + 
    #             Hypertension.composite + DiabetesStatus + SmokerStatus + 
    #             Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
    #             MedHx_CVD + stenose, 
    #           data  =  currentDF, family = binomial(link = "logit"))
    
    fit <- glm(as.factor(currentDF[,TRAIT]) ~ currentDF[,TARGET] + Age + Gender + ORdate_epoch + 
                Hypertension.composite + DiabetesStatus + SmokerStatus + 
                Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
                MedHx_CVD + stenose, 
              data  =  currentDF, family = binomial(link = "logit"))
    
    model_step <- stepAIC(fit, direction = "both", trace = FALSE)
    print(model_step)
    print(summary(fit))
    
    GLM.results.TEMP <- data.frame(matrix(NA, ncol = 16, nrow = 0))
    GLM.results.TEMP[1,] = GLM.BIN(fit, "AERNASE.clin.hdac9", TARGET, TRAIT, verbose = TRUE)
    GLM.results = rbind(GLM.results, GLM.results.TEMP)
  }
}
cat("Edit the column names...\n")
colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
                          "Beta", "s.e.m.",
                          "OR", "low95CI", "up95CI",
                          "Z-value", "P-value", "r^2_l", "r^2_cs", "r^2_nagelkerke", "N", "Model_N", "Perc_Miss")

cat("Correct the variable types...\n")
GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`Z-value` <- as.numeric(GLM.results$`Z-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2_l` <- as.numeric(GLM.results$`r^2_l`)
GLM.results$`r^2_cs` <- as.numeric(GLM.results$`r^2_cs`)
GLM.results$`r^2_nagelkerke` <- as.numeric(GLM.results$`r^2_nagelkerke`)
GLM.results$`N` <- as.numeric(GLM.results$`N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)

# Save the data
cat("Writing results to Excel-file...\n")

### Univariate
write.xlsx(GLM.results,
           file = paste0(OUT_loc, "/",Today,".AERNASE.clin.hdac9.Bin.Multi.",TRAIT_OF_INTEREST,".PlaquePhenotypes.RANK.MODEL2.xlsx"),
           rowNames = FALSE, colNames = TRUE, sheetName = "Bin.Multi.PlaquePheno")

# Removing intermediates
cat("Removing intermediate files...\n")
rm(TRAIT, trait, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)

```


## B. Cross-sectional analysis symptoms

We will perform a cross-sectional analysis between plaque `r TRAIT_OF_INTEREST` expression levels and the 'clinical status' of the plaque in terms of presence of patients' symptoms (symptomatic vs. asymptomatic). The symptoms of interest are:

- stroke
- TIA
- retinal infarction
- amaurosis fugax
- asymptomatic

### Model 1

In this model we correct for _Age_, _Gender_, and _year of surgery_.


```{r CrossSec: symptoms - logistic regression MODEL1 RANK}
GLM.results <- data.frame(matrix(NA, ncol = 16, nrow = 0))
for (target_of_interest in 1:length(TRAITS.TARGET.RANK)) {
  TARGET = TRAITS.TARGET.RANK[target_of_interest]
  cat(paste0("\nAnalysis of ",TARGET,".\n"))
  TRAIT = "AsymptSympt"
    cat(paste0("\n- processing ",TRAIT,"\n\n"))
    currentDF <- as.data.frame(AERNASE.clin.hdac9 %>%
      dplyr::select(., TARGET, TRAIT, COVARIATES_M1) %>%
      filter(complete.cases(.))) %>%
      filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
    # for debug
    # print(DT::datatable(currentDF))
    # print(nrow(currentDF))
    # print(str(currentDF))
    # print(class(currentDF[,TRAIT]))
    ### univariate
     # + Hypertension.composite + DiabetesStatus + SmokerCurrent + 
     #            Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
     #            CAD_history + Stroke_history + Peripheral.interv + stenose
    # fit <- glm(as.factor(currentDF[,TRAIT]) ~ currentDF[,TARGET] + Age + Gender + ORdate_year, 
    #           data  =  currentDF, family = binomial(link = "logit"))

    fit <- glm(as.factor(currentDF[,TRAIT]) ~ currentDF[,TARGET] + Age + Gender + ORdate_epoch, 
              data  =  currentDF, family = binomial(link = "logit"))
    
    model_step <- stepAIC(fit, direction = "both", trace = FALSE)
    print(model_step)
    print(summary(fit))
    
    GLM.results.TEMP <- data.frame(matrix(NA, ncol = 16, nrow = 0))
    GLM.results.TEMP[1,] = GLM.BIN(fit, "AERNASE.clin.hdac9", TARGET, TRAIT, verbose = TRUE)
    GLM.results = rbind(GLM.results, GLM.results.TEMP)
  }
cat("Edit the column names...\n")
colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
                          "Beta", "s.e.m.",
                          "OR", "low95CI", "up95CI",
                          "Z-value", "P-value", "r^2_l", "r^2_cs", "r^2_nagelkerke", "N", "Model_N", "Perc_Miss")

cat("Correct the variable types...\n")
GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`Z-value` <- as.numeric(GLM.results$`Z-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2_l` <- as.numeric(GLM.results$`r^2_l`)
GLM.results$`r^2_cs` <- as.numeric(GLM.results$`r^2_cs`)
GLM.results$`r^2_nagelkerke` <- as.numeric(GLM.results$`r^2_nagelkerke`)
GLM.results$`N` <- as.numeric(GLM.results$`N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)

# Save the data
cat("Writing results to Excel-file...\n")

### Univariate
write.xlsx(GLM.results,
           file = paste0(OUT_loc, "/",Today,".AERNASE.clin.hdac9.Bin.Uni.",TRAIT_OF_INTEREST,".RANK.Symptoms.MODEL1.xlsx"),
           rowNames = FALSE, colNames = TRUE, sheetName = "Bin.Uni.Symptoms")

# Removing intermediates
cat("Removing intermediate files...\n")
rm(TRAIT, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)

```

### Model 2

In this model we correct for _Age_, _Gender_, _Hypertension status_, _Diabetes status_, _current smoker status_, _lipid-lowering drugs (LLDs)_, _antiplatelet medication_, _eGFR (MDRD)_, _BMI_, _MedHx_CVD_ (combination of _CAD history_, _stroke history_, and _peripheral interventions_), and _stenosis._.


```{r CrossSec: symptoms - logistic regression MODEL2 RANK}

GLM.results <- data.frame(matrix(NA, ncol = 16, nrow = 0))
for (target_of_interest in 1:length(TRAITS.TARGET.RANK)) {
  TARGET = TRAITS.TARGET.RANK[target_of_interest]
  cat(paste0("\nAnalysis of ",TARGET,".\n"))
  TRAIT = "AsymptSympt"
    cat(paste0("\n- processing ",TRAIT,"\n\n"))
    currentDF <- as.data.frame(AERNASE.clin.hdac9 %>%
      dplyr::select(., TARGET, TRAIT, COVARIATES_M2) %>%
      filter(complete.cases(.))) %>%
      filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
    # for debug
    # print(DT::datatable(currentDF))
    # print(nrow(currentDF))
    # print(str(currentDF))
    # print(class(currentDF[,TRAIT]))
    ### univariate

    # fit <- glm(as.factor(currentDF[,TRAIT]) ~ currentDF[,TARGET] + Age + Gender + ORdate_year + 
    #              Hypertension.composite + DiabetesStatus + SmokerStatus + 
    #              Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
    #              MedHx_CVD + stenose, 
    #            data  =  currentDF, family = binomial(link = "logit"))
    
    fit <- glm(as.factor(currentDF[,TRAIT]) ~ currentDF[,TARGET] + Age + Gender + ORdate_epoch + 
                 Hypertension.composite + DiabetesStatus + SmokerStatus + 
                 Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
                 MedHx_CVD + stenose, 
               data  =  currentDF, family = binomial(link = "logit"))
    
    model_step <- stepAIC(fit, direction = "both", trace = FALSE)
    print(model_step)
    print(summary(fit))
    
    GLM.results.TEMP <- data.frame(matrix(NA, ncol = 16, nrow = 0))
    GLM.results.TEMP[1,] = GLM.BIN(fit, "AERNASE.clin.hdac9", TARGET, TRAIT, verbose = TRUE)
    GLM.results = rbind(GLM.results, GLM.results.TEMP)
  }
cat("Edit the column names...\n")
colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
                          "Beta", "s.e.m.",
                          "OR", "low95CI", "up95CI",
                          "Z-value", "P-value", "r^2_l", "r^2_cs", "r^2_nagelkerke", "N", "Model_N", "Perc_Miss")

cat("Correct the variable types...\n")
GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`Z-value` <- as.numeric(GLM.results$`Z-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2_l` <- as.numeric(GLM.results$`r^2_l`)
GLM.results$`r^2_cs` <- as.numeric(GLM.results$`r^2_cs`)
GLM.results$`r^2_nagelkerke` <- as.numeric(GLM.results$`r^2_nagelkerke`)
GLM.results$`N` <- as.numeric(GLM.results$`N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)

# Save the data
cat("Writing results to Excel-file...\n")

### Univariate
write.xlsx(GLM.results,
           file = paste0(OUT_loc, "/",Today,".AERNASE.clin.hdac9.Bin.Multi.",TRAIT_OF_INTEREST,".RANK.Symptoms.MODEL2.xlsx"),
           rowNames = FALSE, colNmes = TRUE, sheetName = "Bin.Multi.Symptoms")

# Removing intermediates
cat("Removing intermediate files...\n")
rm(TRAIT, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)

```


## C. Longitudinal analysis secondary clinical outcome

For the longitudinal analyses of plaque `r TRAIT_OF_INTEREST` expression levels and secondary cardiovascular events over a three-year follow-up period. 

The _primary outcome_ is defined as "a composite of fatal or non-fatal myocardial infarction, fatal or non-fatal stroke, ruptured aortic aneurysm, fatal cardiac failure, coronary or peripheral interventions, leg amputation due to vascular causes, and cardiovascular death", i.e. major adverse cardiovascular events (MACE). Variable: `epmajor.3years`, these include:
- myocardial infarction (MI)
- cerebral infarction (CVA/stroke)
- cardiovascular death (exact cause to be investigated)
- cerebral bleeding (CVA/stroke)
- fatal myocardial infarction (MI)
- fatal cerebral infarction
- fatal cerebral bleeding
- sudden death
- fatal heart failure
- fatal aneurysm rupture
- other cardiovascular death..

The _secondary outcomes_ will be 

- incidence of fatal or non-fatal stroke (ischemic and bleeding) - variable: `epstroke.3years`, these include:
  - cerebral infarction (CVA/stroke)
  - cerebral bleeding (CVA/stroke)
  - fatal cerebral infarction
  - fatal cerebral bleeding.
- incidence of acute coronary events (fatal or non-fatal myocardial infarction, coronary interventions) - variable: `epcoronary.3years`, these include:
  - myocardial infarction (MI)
  - coronary angioplasty (PCI/PTCA)
  - cardiovascular death (exact cause to be investigated)
  - coronary bypass (CABG)
  - fatal myocardial infarction (MI)
  - sudden death.
- cardiovascular death - variable: `epcvdeath.3years`, these include:
  - cardiovascular death (exact cause to be investigated)
  - fatal myocardial infarction (MI)
  - fatal cerebral infarction
  - fatal cerebral bleeding
  - sudden death
  - fatal heart failure
  - fatal aneurysm rupture
  - other cardiovascular death..

### 30- and 90-days FU events

We will use 3-year follow-up, but we will also calculate 30 days and 90 days follow-up 'time-to-event' variables. On average there are 365.25 days in a year. We can calculate 30-days and 90-days follow-up time based on the three years follow-up. 

```{r}
cutt.off.30days = (1/365.25) * 30
cutt.off.90days = (1/365.25) * 90

# Fix maximum FU of 30 and 90 days
AEDB.CEA <- AEDB.CEA %>%
  mutate(
    FU.cutt.off.30days = ifelse(max.followup <= cutt.off.30days, max.followup, cutt.off.30days),
    FU.cutt.off.90days = ifelse(max.followup <= cutt.off.90days, max.followup, cutt.off.90days)
  ) 

AEDB.temp <- subset(AEDB.CEA,  select = c("STUDY_NUMBER", "Age", "Gender", "Hospital", "Artery_summary", 
                                      "max.followup", 
                                      "FU.cutt.off.3years",
                                      "FU.cutt.off.30days", 
                                      "FU.cutt.off.90days"))
require(labelled)
AEDB.temp$Gender <- to_factor(AEDB.temp$Gender)
AEDB.temp$Hospital <- to_factor(AEDB.temp$Hospital)
AEDB.temp$Artery_summary <- to_factor(AEDB.temp$Artery_summary)

DT::datatable(AEDB.temp[1:10,], caption = "Excerpt of the whole AEDB.", rownames = FALSE)

rm(AEDB.temp)
```



```{r}

AERNASE.clin.hdac9 <- merge(AERNASE.clin.hdac9, 
                            subset(AEDB.CEA, select = c("STUDY_NUMBER", "max.followup", 
                                                        "FU.cutt.off.3years", "FU.cutt.off.30days", "FU.cutt.off.90days",
                                                        "ep_major_t_3years", "ep_stroke_t_3years", "ep_coronary_t_3years",
                                                        "ep_cvdeath_t_3years",
                                                        "epmajor.3years", "epstroke.3years", "epcoronary.3years", "epcvdeath.3years"
                                                        )), 
                            by.x = "STUDY_NUMBER", by.y = "STUDY_NUMBER", sort = TRUE)


```


```{r Calculate new FU cut-offs: maximum FU}


AERNASE.clin.hdac9.temp <- subset(AERNASE.clin.hdac9,  select = c("STUDY_NUMBER", "Age", "Gender", "Hospital", "Artery_summary", 
                                      "max.followup", 
                                      "FU.cutt.off.3years",
                                      "FU.cutt.off.30days", 
                                      "FU.cutt.off.90days"))
require(labelled)
AERNASE.clin.hdac9.temp$Gender <- to_factor(AERNASE.clin.hdac9.temp$Gender)
AERNASE.clin.hdac9.temp$Hospital <- to_factor(AERNASE.clin.hdac9.temp$Hospital)
AERNASE.clin.hdac9.temp$Artery_summary <- to_factor(AERNASE.clin.hdac9.temp$Artery_summary)

DT::datatable(AERNASE.clin.hdac9.temp[1:10,], caption = "Excerpt of the whole AERNASE.clin.hdac9.", rownames = FALSE)

rm(AERNASE.clin.hdac9.temp)


```

Here we will calculate the new 30- and 90-days follow-up of the events and their event-times of interest:

- MACE (`epmajor.3years`)
- Stroke (`epstroke.3years`)
- Coronary events (`epcoronary.3years`)
- Cardiovascular death (`epcvdeath.3years`)


```{r Calculate new FU cut-offs: times}
avg_days_in_year = 365.25
cutt.off.30days.scaled <- cutt.off.30days * 365.25
cutt.off.90days.scaled <- cutt.off.90days * 365.25
# Event times

AERNASE.clin.hdac9 <- AERNASE.clin.hdac9 %>%
  mutate(
    ep_major_t_30days = ifelse(ep_major_t_3years * avg_days_in_year <= cutt.off.30days.scaled, 
                               ep_major_t_3years * avg_days_in_year, cutt.off.30days.scaled),
    ep_stroke_t_30days = ifelse(ep_stroke_t_3years * avg_days_in_year <= cutt.off.30days.scaled, 
                                ep_stroke_t_3years * avg_days_in_year, cutt.off.30days.scaled),
    ep_coronary_t_30days = ifelse(ep_coronary_t_3years * avg_days_in_year <= cutt.off.30days.scaled, 
                                  ep_coronary_t_3years * avg_days_in_year, cutt.off.30days.scaled),
    ep_cvdeath_t_30days = ifelse(ep_cvdeath_t_3years * avg_days_in_year <= cutt.off.30days.scaled, 
                                 ep_cvdeath_t_3years * avg_days_in_year, cutt.off.30days.scaled),
    ep_major_t_90days = ifelse(ep_major_t_3years * avg_days_in_year <= cutt.off.90days.scaled, 
                               ep_major_t_3years * avg_days_in_year, cutt.off.90days.scaled),
    ep_stroke_t_90days = ifelse(ep_stroke_t_3years * avg_days_in_year <= cutt.off.90days.scaled, 
                                ep_stroke_t_3years * avg_days_in_year, cutt.off.90days.scaled),
    ep_coronary_t_90days = ifelse(ep_coronary_t_3years * avg_days_in_year <= cutt.off.90days.scaled, 
                                  ep_coronary_t_3years * avg_days_in_year, cutt.off.90days.scaled),
    ep_cvdeath_t_90days = ifelse(ep_cvdeath_t_3years * avg_days_in_year <= cutt.off.90days.scaled, 
                                 ep_cvdeath_t_3years * avg_days_in_year, cutt.off.90days.scaled)
  ) 

```


```{r Cox-regressions: new times, message=FALSE, warning=FALSE}

attach(AERNASE.clin.hdac9)
AERNASE.clin.hdac9[,"epmajor.30days"] <- AERNASE.clin.hdac9$epmajor.3years
AERNASE.clin.hdac9$epmajor.30days[epmajor.3years == 1 & ep_major_t_3years > cutt.off.30days] <- 0

AERNASE.clin.hdac9[,"epstroke.30days"] <- AERNASE.clin.hdac9$epstroke.3years
AERNASE.clin.hdac9$epstroke.30days[epstroke.3years == 1 & ep_stroke_t_3years > cutt.off.30days] <- 0

AERNASE.clin.hdac9[,"epcoronary.30days"] <- AERNASE.clin.hdac9$epcoronary.3years
AERNASE.clin.hdac9$epcoronary.30days[epcoronary.3years == 1 & ep_coronary_t_3years > cutt.off.30days] <- 0

AERNASE.clin.hdac9[,"epcvdeath.30days"] <- AERNASE.clin.hdac9$epcvdeath.3years
AERNASE.clin.hdac9$epcvdeath.30days[epcvdeath.3years == 1 & ep_cvdeath_t_3years > cutt.off.30days] <- 0

AERNASE.clin.hdac9[,"epmajor.90days"] <- AERNASE.clin.hdac9$epmajor.3years
AERNASE.clin.hdac9$epmajor.90days[epmajor.3years == 1 & ep_major_t_3years > cutt.off.90days] <- 0

AERNASE.clin.hdac9[,"epstroke.90days"] <- AERNASE.clin.hdac9$epstroke.3years
AERNASE.clin.hdac9$epstroke.90days[epstroke.3years == 1 & ep_stroke_t_3years > cutt.off.90days] <- 0

AERNASE.clin.hdac9[,"epcoronary.90days"] <- AERNASE.clin.hdac9$epcoronary.3years
AERNASE.clin.hdac9$epcoronary.90days[epcoronary.3years == 1 & ep_coronary_t_3years > cutt.off.90days] <- 0

AERNASE.clin.hdac9[,"epcvdeath.90days"] <- AERNASE.clin.hdac9$epcvdeath.3years
AERNASE.clin.hdac9$epcvdeath.90days[epcvdeath.3years == 1 & ep_cvdeath_t_3years > cutt.off.90days] <- 0

detach(AERNASE.clin.hdac9)

AERNASE.clin.hdac9.temp <- subset(AERNASE.clin.hdac9,  select = c("STUDY_NUMBER", "Age", "Gender", "Hospital", "Artery_summary", 
                                      "epmajor.3years", "epstroke.3years", "epcoronary.3years", "epcvdeath.3years",
                                      "epmajor.30days", "epstroke.30days", "epcoronary.30days", "epcvdeath.30days",
                                      "epmajor.90days", "epstroke.90days", "epcoronary.90days", "epcvdeath.90days"))
require(labelled)
AERNASE.clin.hdac9.temp$Gender <- to_factor(AERNASE.clin.hdac9.temp$Gender)
AERNASE.clin.hdac9.temp$Hospital <- to_factor(AERNASE.clin.hdac9.temp$Hospital)
AERNASE.clin.hdac9.temp$Artery_summary <- to_factor(AERNASE.clin.hdac9.temp$Artery_summary)

DT::datatable(AERNASE.clin.hdac9.temp[1:10,], caption = "Excerpt of the whole AERNASE.clin.hdac9.", rownames = FALSE)

rm(AERNASE.clin.hdac9.temp)



```

### Sanity checks

First we do some sanity checks and inventory the time-to-event and event variables.
```{r Cox-regressions: General}
# Reference: https://bioconductor.org/packages/devel/bioc/vignettes/MultiAssayExperiment/inst/doc/QuickStartMultiAssay.html
# If you want to suppress warnings and messages when installing/loading packages
# suppressPackageStartupMessages({})
install.packages.auto("survival")
install.packages.auto("survminer")
install.packages.auto("Hmisc")

cat("* Creating function to summarize Cox regression and prepare container for results.")
# Function to get summary statistics from Cox regression model
COX.STAT <- function(coxfit, DATASET, OUTCOME, target_of_interest){
  cat("Summarizing Cox regression results for '", target_of_interest ,"' and its association to '",OUTCOME,"' in '",DATASET,"'.\n")
  if (nrow(summary(coxfit)$coefficients) == 1) {
    output = c(target_of_interest, rep(NA,8))
    cat("Model not fitted; probably singular.\n")
  }else {
    cat("Collecting data.\n\n")
    cox.sum <- summary(coxfit)
    cox.effectsize = cox.sum$coefficients[1,1]
    cox.SE = cox.sum$coefficients[1,3]
    cox.HReffect = cox.sum$coefficients[1,2]
    cox.CI_low = exp(cox.effectsize - 1.96 * cox.SE)
    cox.CI_up = exp(cox.effectsize + 1.96 * cox.SE)
    cox.zvalue = cox.sum$coefficients[1,4]
    cox.pvalue = cox.sum$coefficients[1,5]
    cox.sample_size = cox.sum$n
    cox.nevents = cox.sum$nevent
    
    output = c(DATASET, OUTCOME, target_of_interest, cox.effectsize, cox.SE, cox.HReffect, cox.CI_low, cox.CI_up, cox.zvalue, cox.pvalue, cox.sample_size, cox.nevents)
    cat("We have collected the following:\n")
    cat("Dataset used..............:", DATASET, "\n")
    cat("Outcome analyzed..........:", OUTCOME, "\n")
    cat("Protein...................:", target_of_interest, "\n")
    cat("Effect size...............:", round(cox.effectsize, 6), "\n")
    cat("Standard error............:", round(cox.SE, 6), "\n")
    cat("Odds ratio (effect size)..:", round(cox.HReffect, 3), "\n")
    cat("Lower 95% CI..............:", round(cox.CI_low, 3), "\n")
    cat("Upper 95% CI..............:", round(cox.CI_up, 3), "\n")
    cat("T-value...................:", round(cox.zvalue, 6), "\n")
    cat("P-value...................:", signif(cox.pvalue, 8), "\n")
    cat("Sample size in model......:", cox.sample_size, "\n")
    cat("Number of events..........:", cox.nevents, "\n")
  }
  return(output)
  print(output)
} 

times = c("ep_major_t_3years", 
          "ep_stroke_t_3years", "ep_coronary_t_3years", "ep_cvdeath_t_3years")

endpoints = c("epmajor.3years", 
              "epstroke.3years", "epcoronary.3years", "epcvdeath.3years")

cat("* Check the cases per event type - for sanity.")
for (events in endpoints){
  require(labelled)
  print(paste0("Printing the summary of: ",events))
  # print(summary(AERNASE.clin.hdac9[,events]))
  print(table(AERNASE.clin.hdac9[,events]))
}

cat("* Check distribution of events over time - for sanity.")
for (eventtimes in times){
  print(paste0("Printing the summary of: ",eventtimes))
  print(summary(AERNASE.clin.hdac9[,eventtimes]))
}

for (eventtime in times){
  
  print(paste0("Printing the distribution of: ",eventtime))
  p <- gghistogram(AERNASE.clin.hdac9, x = eventtime, y = "..count..",
              main = eventtime, bins = 15, 
              xlab = "year", color = uithof_color[16], fill = uithof_color[16], ggtheme = theme_minimal()) 
 print(p)
 ggsave(file = paste0(QC_loc, "/",Today,".AERNASE.clin.hdac9.EventDistributionPerYear.",eventtime,".pdf"), plot = last_plot())
}

times30 = c("ep_major_t_30days", 
          "ep_stroke_t_30days", "ep_coronary_t_30days", "ep_cvdeath_t_30days")

endpoints30 = c("epmajor.30days", 
              "epstroke.30days", "epcoronary.30days", "epcvdeath.30days")

cat("* Check the cases per event type - for sanity.")
for (events in endpoints30){
  print(paste0("Printing the summary of: ",events))
  # print(summary(AERNASE.clin.hdac9[,events]))
  print(table(AERNASE.clin.hdac9[,events]))
}

cat("* Check distribution of events over time - for sanity.")
for (eventtimes in times30){
  print(paste0("Printing the summary of: ",eventtimes))
  print(summary(AERNASE.clin.hdac9[,eventtimes]))
}

for (eventtime in times30){
  
  print(paste0("Printing the distribution of: ",eventtime))
  p <- gghistogram(AERNASE.clin.hdac9, x = eventtime, y = "..count..",
              main = eventtime, bins = 15, 
              xlab = "days", color = uithof_color[16], fill = uithof_color[16], ggtheme = theme_minimal()) 
 print(p)
 ggsave(file = paste0(QC_loc, "/",Today,".AERNASE.clin.hdac9.EventDistributionPer30Days.",eventtime,".pdf"), plot = last_plot())
}

times90 = c("ep_major_t_90days", 
          "ep_stroke_t_90days", "ep_coronary_t_90days", "ep_cvdeath_t_90days")

endpoints90 = c("epmajor.90days", 
              "epstroke.90days", "epcoronary.90days", "epcvdeath.90days")

cat("* Check the cases per event type - for sanity.")
for (events in endpoints90){
  print(paste0("Printing the summary of: ",events))
  # print(summary(AERNASE.clin.hdac9[,events]))
  print(table(AERNASE.clin.hdac9[,events]))
}

cat("* Check distribution of events over time - for sanity.")
for (eventtimes in times90){
  print(paste0("Printing the summary of: ",eventtimes))
  print(summary(AERNASE.clin.hdac9[,eventtimes]))
}

for (eventtime in times90){
  
  print(paste0("Printing the distribution of: ",eventtime))
  p <- gghistogram(AERNASE.clin.hdac9, x = eventtime, y = "..count..",
              main = eventtime, bins = 15, 
              xlab = "days", color = uithof_color[16], fill = uithof_color[16], ggtheme = theme_minimal()) 
 print(p)
 ggsave(file = paste0(QC_loc, "/",Today,".AERNASE.clin.hdac9.EventDistributionPer90Days.",eventtime,".pdf"), plot = last_plot())
}


```


### Cox regressions

Let's perform the actual Cox-regressions. We will apply a couple of models: 

- Model 1: adjusted for age, sex, and year of surgery
- Model 2: adjusted for age, sex, year of surgery, hypertension, diabetes, smoking, LDL-C levels, lipid-lowering drugs, antiplatelet drugs, eGFR, BMI, history of CVD, level of stenosis

#### 3 years follow-up

##### Model 1
```{r Cox-regression Analysis: Simple model}
# Set up a dataframe to receive results
COX.results <- data.frame(matrix(NA, ncol = 12, nrow = 0))

# Looping over each target_of_interest/endpoint/time combination
for (i in 1:length(times)){
  eptime = times[i]
  ep = endpoints[i]
  cat(paste0("* Analyzing the effect of plaque target-of-interest on [",ep,"].\n"))
  cat(" - creating temporary SE for this work.\n")
  TEMP.DF = as.data.frame(AERNASE.clin.hdac9)
  cat(" - making a 'Surv' object and adding this to temporary dataframe.\n")
  TEMP.DF$event <- as.integer(TEMP.DF[,ep])
  TEMP.DF$y <- Surv(time = TEMP.DF[,eptime], event = TEMP.DF$event)
  cat(" - making strata of each of the plaque target-of-interest and start survival analysis.\n")
  
  for (target_of_interest in 1:length(TRAITS.TARGET.RANK)){
    cat(paste0("   > processing [",TRAITS.TARGET.RANK[target_of_interest],"]; ",target_of_interest," out of ",length(TRAITS.TARGET.RANK)," target-of-interest.\n"))
    # splitting into two groups
    TEMP.DF[[ TRAITS.TARGET.RANK[target_of_interest] ]] <- cut2(TEMP.DF[,TRAITS.TARGET.RANK[target_of_interest]], g = 2)
    cat(paste0("   > cross tabulation of ",TRAITS.TARGET.RANK[target_of_interest],"-stratum.\n"))
    show(table(TEMP.DF[[ TRAITS.TARGET.RANK[target_of_interest] ]]))
    
    cat(paste0("\n   > fitting the model for ",TRAITS.TARGET.RANK[target_of_interest],"-stratum.\n"))
    fit <- survfit(as.formula(paste0("y ~ ", TRAITS.TARGET.RANK[target_of_interest])), data = TEMP.DF)
    
    cat(paste0("\n   > make a Kaplan-Meier-shizzle...\n"))
    # make Kaplan-Meier curve and save it
    show(ggsurvplot(fit, data = TEMP.DF,
                    palette = c("#DB003F", "#1290D9"),
                    # palete = c("F59D10", "#DB003F", "#49A01D", "#1290D9"),
                    linetype = c(1,2),
                    # linetype = c(1,2,3,4),
                    # conf.int = FALSE, conf.int.fill = "#595A5C", conf.int.alpha = 0.1,
                    pval = FALSE, pval.method = FALSE, pval.size = 4,
                    risk.table = TRUE, risk.table.y.text = FALSE, tables.y.text.col = TRUE, fontsize = 4,
                    censor = FALSE,
                    legend = "right",
                    legend.title = paste0("",TRAITS.TARGET.RANK[target_of_interest],""),
                    legend.labs = c("low", "high"),
                    title = paste0("Risk of ",ep,""), xlab = "Time [years]", font.main = c(16, "bold", "black")))
    dev.copy2pdf(file = paste0(COX_loc,"/",
                               Today,".AERNASE.clin.hdac9.survival.",ep,".2G.",
                               TRAITS.TARGET.RANK[target_of_interest],".pdf"), width = 12, height = 10, onefile = FALSE)

    cat(paste0("\n   > perform the Cox-regression fashizzle and plot it...\n"))
    ### Do Cox-regression and plot it
    
    ### MODEL 1 (Simple model)
    cox = coxph(Surv(TEMP.DF[,eptime], event) ~ TEMP.DF[[ TRAITS.TARGET.RANK[target_of_interest] ]]+Age+Gender + ORdate_year, data = TEMP.DF)
    coxplot = coxph(Surv(TEMP.DF[,eptime], event) ~ strata(TEMP.DF[[ TRAITS.TARGET.RANK[target_of_interest] ]])+Age+Gender + ORdate_year, data = TEMP.DF)

    plot(survfit(coxplot), main = paste0("Cox proportional hazard of [",ep,"] per [",eptime,"]."),
         # ylim = c(0.2, 1), xlim = c(0,3), col = c("#595A5C", "#DB003F", "#1290D9"),
         ylim = c(0, 1), xlim = c(0,3), col = c("#DB003F", "#1290D9"),
         lty = c(1,2), lwd = 2,
         ylab = "Suvival probability", xlab = "FU time [years]",
         mark.time = FALSE, axes = FALSE, bty = "n")
    legend("topright",
           c("low", "high"),
           title = paste0("",TRAITS.TARGET.RANK[target_of_interest],""),
           col = c("#DB003F", "#1290D9"),
           lty = c(1,2), lwd = 2,
           bty = "n")
    axis(side = 1, at = seq(0, 3, by = 1))
    axis(side = 2, at = seq(0, 1, by = 0.2))
    dev.copy2pdf(file = paste0(COX_loc,"/",
                               Today,".AERNASE.clin.hdac9.Cox.",ep,".2G.",
                               # Today,".AERNASE.clin.hdac9.Cox.",ep,".4G.",
                               TRAITS.TARGET.RANK[target_of_interest],".MODEL1.pdf"), height = 12, width = 10, onefile = TRUE)
    show(summary(cox))

    cat(paste0("\n   > writing the Cox-regression fashizzle to Excel...\n"))

    COX.results.TEMP <- data.frame(matrix(NA, ncol = 12, nrow = 0))
    COX.results.TEMP[1,] = COX.STAT(cox, "AERNASE.clin.hdac9", ep, TRAITS.TARGET.RANK[target_of_interest])
    COX.results = rbind(COX.results, COX.results.TEMP)

  }
}

cat("- Edit the column names...\n")
colnames(COX.results) = c("Dataset", "Outcome", "CpG",
                          "Beta", "s.e.m.",
                          "HR", "low95CI", "up95CI",
                          "Z-value", "P-value", "SampleSize", "N_events")

cat("- Correct the variable types...\n")
COX.results$Beta <- as.numeric(COX.results$Beta)
COX.results$s.e.m. <- as.numeric(COX.results$s.e.m.)
COX.results$HR <- as.numeric(COX.results$HR)
COX.results$low95CI <- as.numeric(COX.results$low95CI)
COX.results$up95CI <- as.numeric(COX.results$up95CI)
COX.results$`Z-value` <- as.numeric(COX.results$`Z-value`)
COX.results$`P-value` <- as.numeric(COX.results$`P-value`)
COX.results$SampleSize <- as.numeric(COX.results$SampleSize)
COX.results$N_events <- as.numeric(COX.results$N_events)

AERNASE.clin.hdac9.COX.results <- COX.results

# Save the data
cat("- Writing results to Excel-file...\n")
head.style <- createStyle(textDecoration = "BOLD")
write.xlsx(AERNASE.clin.hdac9.COX.results,
           file = paste0(OUT_loc, "/",Today,".AERNASE.clin.hdac9.Cox.2G.MODEL1.xlsx"),
           creator = "Sander W. van der Laan",
           sheetName = "Results", headerStyle = head.style,
           rowNames = FALSE, colNames = TRUE, overwrite = TRUE)

# Removing intermediates
cat("- Removing intermediate files...\n")
rm(TEMP.DF, target_of_interest, fit, cox, coxplot, COX.results, COX.results.TEMP, head.style, AERNASE.clin.hdac9.COX.results)

```

##### Model 2
```{r Cox-regression Analysis: MODEL 2}
# Set up a dataframe to receive results
COX.results <- data.frame(matrix(NA, ncol = 12, nrow = 0))

# Looping over each target_of_interest/endpoint/time combination
for (i in 1:length(times)){
  eptime = times[i]
  ep = endpoints[i]
  cat(paste0("* Analyzing the effect of plaque target-of-interest on [",ep,"].\n"))
  cat(" - creating temporary SE for this work.\n")
  TEMP.DF = as.data.frame(AERNASE.clin.hdac9)
  cat(" - making a 'Surv' object and adding this to temporary dataframe.\n")
  TEMP.DF$event <- as.integer(TEMP.DF[,ep])
  #as.integer(TEMP.DF[,ep] == "Excluded")

  TEMP.DF$y <- Surv(time = TEMP.DF[,eptime], event = TEMP.DF$event)
  cat(" - making strata of each of the plaque target-of-interest and start survival analysis.\n")
  
  for (target_of_interest in 1:length(TRAITS.TARGET.RANK)){
    cat(paste0("   > processing [",TRAITS.TARGET.RANK[target_of_interest],"]; ",target_of_interest," out of ",length(TRAITS.TARGET.RANK)," target-of-interest.\n"))
    # splitting into two groups
    TEMP.DF[[ TRAITS.TARGET.RANK[target_of_interest] ]] <- cut2(TEMP.DF[,TRAITS.TARGET.RANK[target_of_interest]], g = 2)
    cat(paste0("   > cross tabulation of ",TRAITS.TARGET.RANK[target_of_interest],"-stratum.\n"))
    show(table(TEMP.DF[[ TRAITS.TARGET.RANK[target_of_interest] ]]))
    
    cat(paste0("\n   > fitting the model for ",TRAITS.TARGET.RANK[target_of_interest],"-stratum.\n"))
    fit <- survfit(as.formula(paste0("y ~ ", TRAITS.TARGET.RANK[target_of_interest])), data = TEMP.DF)
    
    cat(paste0("\n   > make a Kaplan-Meier-shizzle...\n"))
    # make Kaplan-Meier curve and save it
    show(ggsurvplot(fit, data = TEMP.DF,
                    palette = c("#DB003F", "#1290D9"),
                    # palete = c("F59D10", "#DB003F", "#49A01D", "#1290D9"),
                    linetype = c(1,2),
                    # linetype = c(1,2,3,4),
                    # conf.int = FALSE, conf.int.fill = "#595A5C", conf.int.alpha = 0.1,
                    pval = FALSE, pval.method = FALSE, pval.size = 4,
                    risk.table = TRUE, risk.table.y.text = FALSE, tables.y.text.col = TRUE, fontsize = 4,
                    censor = FALSE,
                    legend = "right",
                    legend.title = paste0("",TRAITS.TARGET.RANK[target_of_interest],""),
                    legend.labs = c("low", "high"),
                    title = paste0("Risk of ",ep,""), xlab = "Time [years]", font.main = c(16, "bold", "black")))
    dev.copy2pdf(file = paste0(COX_loc,"/",
                               Today,".AERNASE.clin.hdac9.survival.",ep,".2G.",
                               TRAITS.TARGET.RANK[target_of_interest],".pdf"), width = 12, height = 10, onefile = FALSE)

    cat(paste0("\n   > perform the Cox-regression fashizzle and plot it...\n"))
    ### Do Cox-regression and plot it
    
    ### MODEL 2 adjusted for age, sex, hypertension, diabetes, smoking, LDL-C levels, lipid-lowering drugs, antiplatelet drugs, eGFR, BMI, history of CVD, level of stenosis
    cox = coxph(Surv(TEMP.DF[,eptime], event) ~ TEMP.DF[[ TRAITS.TARGET.RANK[target_of_interest] ]]+Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus + SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + MedHx_CVD + stenose, data = TEMP.DF)
    coxplot = coxph(Surv(TEMP.DF[,eptime], event) ~ strata(TEMP.DF[[ TRAITS.TARGET.RANK[target_of_interest] ]])+Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus + SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + MedHx_CVD + stenose, data = TEMP.DF)

  
    plot(survfit(coxplot), main = paste0("Cox proportional hazard of [",ep,"] per [",eptime,"]."),
         # ylim = c(0.2, 1), xlim = c(0,3), col = c("#595A5C", "#DB003F", "#1290D9"),
         ylim = c(0, 1), xlim = c(0,3), col = c("#DB003F", "#1290D9"),
         lty = c(1,2), lwd = 2,
         ylab = "Suvival probability", xlab = "FU time [years]",
         mark.time = FALSE, axes = FALSE, bty = "n")
    legend("topright",
           c("low", "high"),
           title = paste0("",TRAITS.TARGET.RANK[target_of_interest],""),
           col = c("#DB003F", "#1290D9"),
           lty = c(1,2), lwd = 2,
           bty = "n")
    axis(side = 1, at = seq(0, 3, by = 1))
    axis(side = 2, at = seq(0, 1, by = 0.2))
    dev.copy2pdf(file = paste0(COX_loc,"/",
                               Today,".AERNASE.clin.hdac9.Cox.",ep,".2G.",
                               # Today,".AERNASE.clin.hdac9.Cox.",ep,".4G.",
                               TRAITS.TARGET.RANK[target_of_interest],".MODEL2.pdf"), height = 12, width = 10, onefile = TRUE)

    show(summary(cox))

    cat(paste0("\n   > writing the Cox-regression fashizzle to Excel...\n"))

    COX.results.TEMP <- data.frame(matrix(NA, ncol = 12, nrow = 0))
    COX.results.TEMP[1,] = COX.STAT(cox, "AERNASE.clin.hdac9", ep, TRAITS.TARGET.RANK[target_of_interest])
    COX.results = rbind(COX.results, COX.results.TEMP)

  }
}

cat("- Edit the column names...\n")
colnames(COX.results) = c("Dataset", "Outcome", "CpG",
                          "Beta", "s.e.m.",
                          "HR", "low95CI", "up95CI",
                          "Z-value", "P-value", "SampleSize", "N_events")

cat("- Correct the variable types...\n")
COX.results$Beta <- as.numeric(COX.results$Beta)
COX.results$s.e.m. <- as.numeric(COX.results$s.e.m.)
COX.results$HR <- as.numeric(COX.results$HR)
COX.results$low95CI <- as.numeric(COX.results$low95CI)
COX.results$up95CI <- as.numeric(COX.results$up95CI)
COX.results$`Z-value` <- as.numeric(COX.results$`Z-value`)
COX.results$`P-value` <- as.numeric(COX.results$`P-value`)
COX.results$SampleSize <- as.numeric(COX.results$SampleSize)
COX.results$N_events <- as.numeric(COX.results$N_events)

AERNASE.clin.hdac9.COX.results <- COX.results

# Save the data
cat("- Writing results to Excel-file...\n")
head.style <- createStyle(textDecoration = "BOLD")
write.xlsx(AERNASE.clin.hdac9.COX.results,
           file = paste0(OUT_loc, "/",Today,".AERNASE.clin.hdac9.Cox.2G.MODEL2.xlsx"),
           creator = "Sander W. van der Laan",
           sheetName = "Results", headerStyle = head.style,
           rowNames = FALSE, colNames = TRUE, overwrite = TRUE)

# Removing intermediates
cat("- Removing intermediate files...\n")
rm(TEMP.DF, target_of_interest, fit, cox, coxplot, COX.results, COX.results.TEMP, head.style, AERNASE.clin.hdac9.COX.results)


```


#### 30-days follow-up

##### Model 1
```{r Cox-regression Analysis: Simple model, 30 days}
# Set up a dataframe to receive results
COX.results <- data.frame(matrix(NA, ncol = 12, nrow = 0))

# Looping over each target_of_interest/endpoint/time combination
for (i in 1:length(times30)){
  eptime = times30[i]
  ep = endpoints30[i]
  cat(paste0("* Analyzing the effect of plaque target-of-interest on [",ep,"].\n"))
  cat(" - creating temporary SE for this work.\n")
  TEMP.DF = as.data.frame(AERNASE.clin.hdac9)
  cat(" - making a 'Surv' object and adding this to temporary dataframe.\n")
  TEMP.DF$event <- as.integer(TEMP.DF[,ep])
  TEMP.DF$y <- Surv(time = TEMP.DF[,eptime], event = TEMP.DF$event)
  cat(" - making strata of each of the plaque target-of-interest and start survival analysis.\n")
  
  for (target_of_interest in 1:length(TRAITS.TARGET.RANK)){
    cat(paste0("   > processing [",TRAITS.TARGET.RANK[target_of_interest],"]; ",target_of_interest," out of ",length(TRAITS.TARGET.RANK)," target-of-interest.\n"))
    # splitting into two groups
    TEMP.DF[[ TRAITS.TARGET.RANK[target_of_interest] ]] <- cut2(TEMP.DF[,TRAITS.TARGET.RANK[target_of_interest]], g = 2)
    cat(paste0("   > cross tabulation of ",TRAITS.TARGET.RANK[target_of_interest],"-stratum.\n"))
    show(table(TEMP.DF[[ TRAITS.TARGET.RANK[target_of_interest] ]]))
    
    cat(paste0("\n   > fitting the model for ",TRAITS.TARGET.RANK[target_of_interest],"-stratum.\n"))
    fit <- survfit(as.formula(paste0("y ~ ", TRAITS.TARGET.RANK[target_of_interest])), data = TEMP.DF)
    
    cat(paste0("\n   > make a Kaplan-Meier-shizzle...\n"))
    # make Kaplan-Meier curve and save it
    show(ggsurvplot(fit, data = TEMP.DF,
                    palette = c("#DB003F", "#1290D9"),
                    # palete = c("F59D10", "#DB003F", "#49A01D", "#1290D9"),
                    linetype = c(1,2),
                    ylim = c(0.75, 1),
                    # linetype = c(1,2,3,4),
                    # conf.int = FALSE, conf.int.fill = "#595A5C", conf.int.alpha = 0.1,
                    pval = FALSE, pval.method = FALSE, pval.size = 4,
                    risk.table = TRUE, risk.table.y.text = FALSE, tables.y.text.col = TRUE, fontsize = 4,
                    censor = FALSE,
                    legend = "right",
                    legend.title = paste0("",TRAITS.TARGET.RANK[target_of_interest],""),
                    legend.labs = c("low", "high"),
                    title = paste0("Risk of ",ep,""), xlab = "Time [days]", font.main = c(16, "bold", "black")))
    dev.copy2pdf(file = paste0(COX_loc,"/",
                               Today,".AERNASE.clin.hdac9.survival.",ep,".2G.",
                               TRAITS.TARGET.RANK[target_of_interest],".30days.pdf"), width = 12, height = 10, onefile = FALSE)

    cat(paste0("\n   > perform the Cox-regression fashizzle and plot it...\n"))
    ### Do Cox-regression and plot it
    
    ### MODEL 1 (Simple model)
    cox = coxph(Surv(TEMP.DF[,eptime], event) ~ TEMP.DF[[ TRAITS.TARGET.RANK[target_of_interest] ]]+Age+Gender + ORdate_year, data = TEMP.DF)
    coxplot = coxph(Surv(TEMP.DF[,eptime], event) ~ strata(TEMP.DF[[ TRAITS.TARGET.RANK[target_of_interest] ]])+Age+Gender + ORdate_year, data = TEMP.DF)

    plot(survfit(coxplot), main = paste0("Cox proportional hazard of [",ep,"] per [",eptime,"]."),
         ylim = c(0.75, 1), xlim = c(0,3), col = c("#595A5C", "#DB003F", "#1290D9"),
         # ylim = c(0, 1), xlim = c(0,3), col = c("#DB003F", "#1290D9"),
         lty = c(1,2), lwd = 2,
         ylab = "Suvival probability", xlab = "FU time [days]",
         mark.time = FALSE, axes = FALSE, bty = "n")
    legend("topright",
           c("low", "high"),
           title = paste0("",TRAITS.TARGET.RANK[target_of_interest],""),
           col = c("#DB003F", "#1290D9"),
           lty = c(1,2), lwd = 2,
           bty = "n")
    axis(side = 1, at = seq(0, 3, by = 1))
    axis(side = 2, at = seq(0, 1, by = 0.2))
    dev.copy2pdf(file = paste0(COX_loc,"/",
                               Today,".AERNASE.clin.hdac9.Cox.",ep,".2G.",
                               # Today,".AERNASE.clin.hdac9.Cox.",ep,".4G.",
                               TRAITS.TARGET.RANK[target_of_interest],".MODEL1.30days.pdf"), height = 12, width = 10, onefile = TRUE)
    show(summary(cox))

    cat(paste0("\n   > writing the Cox-regression fashizzle to Excel...\n"))

    COX.results.TEMP <- data.frame(matrix(NA, ncol = 12, nrow = 0))
    COX.results.TEMP[1,] = COX.STAT(cox, "AERNASE.clin.hdac9", ep, TRAITS.TARGET.RANK[target_of_interest])
    COX.results = rbind(COX.results, COX.results.TEMP)

  }
}

cat("- Edit the column names...\n")
colnames(COX.results) = c("Dataset", "Outcome", "CpG",
                          "Beta", "s.e.m.",
                          "HR", "low95CI", "up95CI",
                          "Z-value", "P-value", "SampleSize", "N_events")

cat("- Correct the variable types...\n")
COX.results$Beta <- as.numeric(COX.results$Beta)
COX.results$s.e.m. <- as.numeric(COX.results$s.e.m.)
COX.results$HR <- as.numeric(COX.results$HR)
COX.results$low95CI <- as.numeric(COX.results$low95CI)
COX.results$up95CI <- as.numeric(COX.results$up95CI)
COX.results$`Z-value` <- as.numeric(COX.results$`Z-value`)
COX.results$`P-value` <- as.numeric(COX.results$`P-value`)
COX.results$SampleSize <- as.numeric(COX.results$SampleSize)
COX.results$N_events <- as.numeric(COX.results$N_events)

AERNASE.clin.hdac9.COX.results <- COX.results

# Save the data
library(openxlsx)
cat("- Writing results to Excel-file...\n")
head.style <- createStyle(textDecoration = "BOLD")
write.xlsx(AERNASE.clin.hdac9.COX.results,
           file = paste0(OUT_loc, "/",Today,".AERNASE.clin.hdac9.Cox.2G.MODEL1.30days.xlsx"),
           creator = "Sander W. van der Laan",
           sheetName = "Results", headerStyle = head.style,
           rowNames = FALSE, colnames = TRUE, overwrite = TRUE)

# Removing intermediates
cat("- Removing intermediate files...\n")
rm(TEMP.DF, target_of_interest, fit, cox, coxplot, COX.results, COX.results.TEMP, head.style, AERNASE.clin.hdac9.COX.results)

```

##### Model 2
```{r Cox-regression Analysis: MODEL 2, 30 days}
# Set up a dataframe to receive results
COX.results <- data.frame(matrix(NA, ncol = 12, nrow = 0))

# Looping over each target_of_interest/endpoint/time combination
for (i in 1:length(times30)){
  eptime = times30[i]
  ep = endpoints30[i]
  cat(paste0("* Analyzing the effect of plaque target-of-interest on [",ep,"].\n"))
  cat(" - creating temporary SE for this work.\n")
  TEMP.DF = as.data.frame(AERNASE.clin.hdac9)
  cat(" - making a 'Surv' object and adding this to temporary dataframe.\n")
  TEMP.DF$event <- as.integer(TEMP.DF[,ep])
  #as.integer(TEMP.DF[,ep] == "Excluded")

  TEMP.DF$y <- Surv(time = TEMP.DF[,eptime], event = TEMP.DF$event)
  cat(" - making strata of each of the plaque target-of-interest and start survival analysis.\n")
  
  for (target_of_interest in 1:length(TRAITS.TARGET.RANK)){
    cat(paste0("   > processing [",TRAITS.TARGET.RANK[target_of_interest],"]; ",target_of_interest," out of ",length(TRAITS.TARGET.RANK)," target-of-interest.\n"))
    # splitting into two groups
    TEMP.DF[[ TRAITS.TARGET.RANK[target_of_interest] ]] <- cut2(TEMP.DF[,TRAITS.TARGET.RANK[target_of_interest]], g = 2)
    cat(paste0("   > cross tabulation of ",TRAITS.TARGET.RANK[target_of_interest],"-stratum.\n"))
    show(table(TEMP.DF[[ TRAITS.TARGET.RANK[target_of_interest] ]]))
    
    cat(paste0("\n   > fitting the model for ",TRAITS.TARGET.RANK[target_of_interest],"-stratum.\n"))
    fit <- survfit(as.formula(paste0("y ~ ", TRAITS.TARGET.RANK[target_of_interest])), data = TEMP.DF)
    
    cat(paste0("\n   > make a Kaplan-Meier-shizzle...\n"))
    # make Kaplan-Meier curve and save it
    show(ggsurvplot(fit, data = TEMP.DF,
                    palette = c("#DB003F", "#1290D9"),
                    # palete = c("F59D10", "#DB003F", "#49A01D", "#1290D9"),
                    linetype = c(1,2),
                    ylim = c(0.75, 1),
                    # linetype = c(1,2,3,4),
                    # conf.int = FALSE, conf.int.fill = "#595A5C", conf.int.alpha = 0.1,
                    pval = FALSE, pval.method = FALSE, pval.size = 4,
                    risk.table = TRUE, risk.table.y.text = FALSE, tables.y.text.col = TRUE, fontsize = 4,
                    censor = FALSE,
                    legend = "right",
                    legend.title = paste0("",TRAITS.TARGET.RANK[target_of_interest],""),
                    legend.labs = c("low", "high"),
                    title = paste0("Risk of ",ep,""), xlab = "Time [days]", font.main = c(16, "bold", "black")))
    dev.copy2pdf(file = paste0(COX_loc,"/",
                               Today,".AERNASE.clin.hdac9.survival.",ep,".2G.",
                               TRAITS.TARGET.RANK[target_of_interest],".30days.pdf"), width = 12, height = 10, onefile = FALSE)

    cat(paste0("\n   > perform the Cox-regression fashizzle and plot it...\n"))
    ### Do Cox-regression and plot it
    
    ### MODEL 2 adjusted for age, sex, hypertension, diabetes, smoking, LDL-C levels, lipid-lowering drugs, antiplatelet drugs, eGFR, BMI, history of CVD, level of stenosis
    cox = coxph(Surv(TEMP.DF[,eptime], event) ~ TEMP.DF[[ TRAITS.TARGET.RANK[target_of_interest] ]]+Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus + SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + MedHx_CVD + stenose, data = TEMP.DF)
    coxplot = coxph(Surv(TEMP.DF[,eptime], event) ~ strata(TEMP.DF[[ TRAITS.TARGET.RANK[target_of_interest] ]])+Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus + SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + MedHx_CVD + stenose, data = TEMP.DF)

  
    plot(survfit(coxplot), main = paste0("Cox proportional hazard of [",ep,"] per [",eptime,"]."),
         ylim = c(0.75, 1), xlim = c(0,3), col = c("#DB003F", "#1290D9"),
         # ylim = c(0, 1), xlim = c(0,3), col = c("#DB003F", "#1290D9"),
         lty = c(1,2), lwd = 2,
         ylab = "Suvival probability", xlab = "FU time [days]",
         mark.time = FALSE, axes = FALSE, bty = "n")
    legend("topright",
           c("low", "high"),
           title = paste0("",TRAITS.TARGET.RANK[target_of_interest],""),
           col = c("#DB003F", "#1290D9"),
           lty = c(1,2), lwd = 2,
           bty = "n")
    axis(side = 1, at = seq(0, 3, by = 1))
    axis(side = 2, at = seq(0, 1, by = 0.2))
    dev.copy2pdf(file = paste0(COX_loc,"/",
                               Today,".AERNASE.clin.hdac9.Cox.",ep,".2G.",
                               # Today,".AERNASE.clin.hdac9.Cox.",ep,".4G.",
                               TRAITS.TARGET.RANK[target_of_interest],".MODEL2.30days.pdf"), height = 12, width = 10, onefile = TRUE)

    show(summary(cox))

    cat(paste0("\n   > writing the Cox-regression fashizzle to Excel...\n"))

    COX.results.TEMP <- data.frame(matrix(NA, ncol = 12, nrow = 0))
    COX.results.TEMP[1,] = COX.STAT(cox, "AERNASE.clin.hdac9", ep, TRAITS.TARGET.RANK[target_of_interest])
    COX.results = rbind(COX.results, COX.results.TEMP)

  }
}

cat("- Edit the column names...\n")
colnames(COX.results) = c("Dataset", "Outcome", "CpG",
                          "Beta", "s.e.m.",
                          "HR", "low95CI", "up95CI",
                          "Z-value", "P-value", "SampleSize", "N_events")

cat("- Correct the variable types...\n")
COX.results$Beta <- as.numeric(COX.results$Beta)
COX.results$s.e.m. <- as.numeric(COX.results$s.e.m.)
COX.results$HR <- as.numeric(COX.results$HR)
COX.results$low95CI <- as.numeric(COX.results$low95CI)
COX.results$up95CI <- as.numeric(COX.results$up95CI)
COX.results$`Z-value` <- as.numeric(COX.results$`Z-value`)
COX.results$`P-value` <- as.numeric(COX.results$`P-value`)
COX.results$SampleSize <- as.numeric(COX.results$SampleSize)
COX.results$N_events <- as.numeric(COX.results$N_events)

AERNASE.clin.hdac9.COX.results <- COX.results

# Save the data
cat("- Writing results to Excel-file...\n")
head.style <- createStyle(textDecoration = "BOLD")

write.xlsx(AERNASE.clin.hdac9.COX.results,
           file = paste0(OUT_loc, "/",Today,".AERNASE.clin.hdac9.Cox.2G.MODEL2.30days.xlsx"),
           creator = "Sander W. van der Laan",
           sheetName = "Results", headerStyle = head.style,
           rowNames = FALSE, colNames = TRUE, overwrite = TRUE)

# Removing intermediates
cat("- Removing intermediate files...\n")
rm(TEMP.DF, target_of_interest, fit, cox, coxplot, COX.results, COX.results.TEMP, head.style, AERNASE.clin.hdac9.COX.results)


```


#### 90-days follow-up

##### Model 1 
```{r Cox-regression Analysis: Simple model, 90 days}
# Set up a dataframe to receive results
COX.results <- data.frame(matrix(NA, ncol = 12, nrow = 0))

# Looping over each target_of_interest/endpoint/time combination
for (i in 1:length(times90)){
  eptime = times90[i]
  ep = endpoints90[i]
  cat(paste0("* Analyzing the effect of plaque target-of-interest on [",ep,"].\n"))
  cat(" - creating temporary SE for this work.\n")
  TEMP.DF = as.data.frame(AERNASE.clin.hdac9)
  cat(" - making a 'Surv' object and adding this to temporary dataframe.\n")
  TEMP.DF$event <- as.integer(TEMP.DF[,ep])
  TEMP.DF$y <- Surv(time = TEMP.DF[,eptime], event = TEMP.DF$event)
  cat(" - making strata of each of the plaque target-of-interest and start survival analysis.\n")
  
  for (target_of_interest in 1:length(TRAITS.TARGET.RANK)){
    cat(paste0("   > processing [",TRAITS.TARGET.RANK[target_of_interest],"]; ",target_of_interest," out of ",length(TRAITS.TARGET.RANK)," target-of-interest.\n"))
    # splitting into two groups
    TEMP.DF[[ TRAITS.TARGET.RANK[target_of_interest] ]] <- cut2(TEMP.DF[,TRAITS.TARGET.RANK[target_of_interest]], g = 2)
    cat(paste0("   > cross tabulation of ",TRAITS.TARGET.RANK[target_of_interest],"-stratum.\n"))
    show(table(TEMP.DF[[ TRAITS.TARGET.RANK[target_of_interest] ]]))
    
    cat(paste0("\n   > fitting the model for ",TRAITS.TARGET.RANK[target_of_interest],"-stratum.\n"))
    fit <- survfit(as.formula(paste0("y ~ ", TRAITS.TARGET.RANK[target_of_interest])), data = TEMP.DF)
    
    cat(paste0("\n   > make a Kaplan-Meier-shizzle...\n"))
    # make Kaplan-Meier curve and save it
    show(ggsurvplot(fit, data = TEMP.DF,
                    palette = c("#DB003F", "#1290D9"),
                    # palete = c("F59D10", "#DB003F", "#49A01D", "#1290D9"),
                    linetype = c(1,2),
                    ylim = c(0.75, 1),
                    # linetype = c(1,2,3,4),
                    # conf.int = FALSE, conf.int.fill = "#595A5C", conf.int.alpha = 0.1,
                    pval = FALSE, pval.method = FALSE, pval.size = 4,
                    risk.table = TRUE, risk.table.y.text = FALSE, tables.y.text.col = TRUE, fontsize = 4,
                    censor = FALSE,
                    legend = "right",
                    legend.title = paste0("",TRAITS.TARGET.RANK[target_of_interest],""),
                    legend.labs = c("low", "high"),
                    title = paste0("Risk of ",ep,""), xlab = "Time [days]", font.main = c(16, "bold", "black")))
    dev.copy2pdf(file = paste0(COX_loc,"/",
                               Today,".AERNASE.clin.hdac9.survival.",ep,".2G.",
                               TRAITS.TARGET.RANK[target_of_interest],".90days.pdf"), width = 12, height = 10, onefile = FALSE)

    cat(paste0("\n   > perform the Cox-regression fashizzle and plot it...\n"))
    ### Do Cox-regression and plot it
    
    ### MODEL 1 (Simple model)
    cox = coxph(Surv(TEMP.DF[,eptime], event) ~ TEMP.DF[[ TRAITS.TARGET.RANK[target_of_interest] ]]+Age+Gender + ORdate_year, data = TEMP.DF)
    coxplot = coxph(Surv(TEMP.DF[,eptime], event) ~ strata(TEMP.DF[[ TRAITS.TARGET.RANK[target_of_interest] ]])+Age+Gender + ORdate_year, data = TEMP.DF)

    plot(survfit(coxplot), main = paste0("Cox proportional hazard of [",ep,"] per [",eptime,"]."),
         ylim = c(0.75, 1), xlim = c(0,3), col = c("#595A5C", "#DB003F", "#1290D9"),
         # ylim = c(0, 1), xlim = c(0,3), col = c("#DB003F", "#1290D9"),
         lty = c(1,2), lwd = 2,
         ylab = "Suvival probability", xlab = "FU time [days]",
         mark.time = FALSE, axes = FALSE, bty = "n")
    legend("topright",
           c("low", "high"),
           title = paste0("",TRAITS.TARGET.RANK[target_of_interest],""),
           col = c("#DB003F", "#1290D9"),
           lty = c(1,2), lwd = 2,
           bty = "n")
    axis(side = 1, at = seq(0, 3, by = 1))
    axis(side = 2, at = seq(0, 1, by = 0.2))
    dev.copy2pdf(file = paste0(COX_loc,"/",
                               Today,".AERNASE.clin.hdac9.Cox.",ep,".2G.",
                               # Today,".AERNASE.clin.hdac9.Cox.",ep,".4G.",
                               TRAITS.TARGET.RANK[target_of_interest],".MODEL1.90days.pdf"), height = 12, width = 10, onefile = TRUE)
    show(summary(cox))

    cat(paste0("\n   > writing the Cox-regression fashizzle to Excel...\n"))

    COX.results.TEMP <- data.frame(matrix(NA, ncol = 12, nrow = 0))
    COX.results.TEMP[1,] = COX.STAT(cox, "AERNASE.clin.hdac9", ep, TRAITS.TARGET.RANK[target_of_interest])
    COX.results = rbind(COX.results, COX.results.TEMP)

  }
}

cat("- Edit the column names...\n")
colnames(COX.results) = c("Dataset", "Outcome", "CpG",
                          "Beta", "s.e.m.",
                          "HR", "low95CI", "up95CI",
                          "Z-value", "P-value", "SampleSize", "N_events")

cat("- Correct the variable types...\n")
COX.results$Beta <- as.numeric(COX.results$Beta)
COX.results$s.e.m. <- as.numeric(COX.results$s.e.m.)
COX.results$HR <- as.numeric(COX.results$HR)
COX.results$low95CI <- as.numeric(COX.results$low95CI)
COX.results$up95CI <- as.numeric(COX.results$up95CI)
COX.results$`Z-value` <- as.numeric(COX.results$`Z-value`)
COX.results$`P-value` <- as.numeric(COX.results$`P-value`)
COX.results$SampleSize <- as.numeric(COX.results$SampleSize)
COX.results$N_events <- as.numeric(COX.results$N_events)

AERNASE.clin.hdac9.COX.results <- COX.results

# Save the data
library(openxlsx)
cat("- Writing results to Excel-file...\n")
head.style <- createStyle(textDecoration = "BOLD")
write.xlsx(AERNASE.clin.hdac9.COX.results,
           file = paste0(OUT_loc, "/",Today,".AERNASE.clin.hdac9.Cox.2G.MODEL1.90days.xlsx"),
           creator = "Sander W. van der Laan",
           sheetName = "Results", headerStyle = head.style,
           rowNames = FALSE, colNames = TRUE, overwrite = TRUE)

# Removing intermediates
cat("- Removing intermediate files...\n")
rm(TEMP.DF, target_of_interest, fit, cox, coxplot, COX.results, COX.results.TEMP, head.style, AERNASE.clin.hdac9.COX.results)

```

##### Model 2
```{r Cox-regression Analysis: MODEL 2, 90 days}
# Set up a dataframe to receive results
COX.results <- data.frame(matrix(NA, ncol = 12, nrow = 0))

# Looping over each target_of_interest/endpoint/time combination
for (i in 1:length(times90)){
  eptime = times90[i]
  ep = endpoints90[i]
  cat(paste0("* Analyzing the effect of plaque target-of-interest on [",ep,"].\n"))
  cat(" - creating temporary SE for this work.\n")
  TEMP.DF = as.data.frame(AERNASE.clin.hdac9)
  cat(" - making a 'Surv' object and adding this to temporary dataframe.\n")
  TEMP.DF$event <- as.integer(TEMP.DF[,ep])
  #as.integer(TEMP.DF[,ep] == "Excluded")

  TEMP.DF$y <- Surv(time = TEMP.DF[,eptime], event = TEMP.DF$event)
  cat(" - making strata of each of the plaque target-of-interest and start survival analysis.\n")
  
  for (target_of_interest in 1:length(TRAITS.TARGET.RANK)){
    cat(paste0("   > processing [",TRAITS.TARGET.RANK[target_of_interest],"]; ",target_of_interest," out of ",length(TRAITS.TARGET.RANK)," target-of-interest.\n"))
    # splitting into two groups
    TEMP.DF[[ TRAITS.TARGET.RANK[target_of_interest] ]] <- cut2(TEMP.DF[,TRAITS.TARGET.RANK[target_of_interest]], g = 2)
    cat(paste0("   > cross tabulation of ",TRAITS.TARGET.RANK[target_of_interest],"-stratum.\n"))
    show(table(TEMP.DF[[ TRAITS.TARGET.RANK[target_of_interest] ]]))
    
    cat(paste0("\n   > fitting the model for ",TRAITS.TARGET.RANK[target_of_interest],"-stratum.\n"))
    fit <- survfit(as.formula(paste0("y ~ ", TRAITS.TARGET.RANK[target_of_interest])), data = TEMP.DF)
    
    cat(paste0("\n   > make a Kaplan-Meier-shizzle...\n"))
    # make Kaplan-Meier curve and save it
    show(ggsurvplot(fit, data = TEMP.DF,
                    palette = c("#DB003F", "#1290D9"),
                    # palete = c("F59D10", "#DB003F", "#49A01D", "#1290D9"),
                    linetype = c(1,2),
                    ylim = c(0.75, 1),
                    # linetype = c(1,2,3,4),
                    # conf.int = FALSE, conf.int.fill = "#595A5C", conf.int.alpha = 0.1,
                    pval = FALSE, pval.method = FALSE, pval.size = 4,
                    risk.table = TRUE, risk.table.y.text = FALSE, tables.y.text.col = TRUE, fontsize = 4,
                    censor = FALSE,
                    legend = "right",
                    legend.title = paste0("",TRAITS.TARGET.RANK[target_of_interest],""),
                    legend.labs = c("low", "high"),
                    title = paste0("Risk of ",ep,""), xlab = "Time [days]", font.main = c(16, "bold", "black")))
    dev.copy2pdf(file = paste0(COX_loc,"/",
                               Today,".AERNASE.clin.hdac9.survival.",ep,".2G.",
                               TRAITS.TARGET.RANK[target_of_interest],".90days.pdf"), width = 12, height = 10, onefile = FALSE)

    cat(paste0("\n   > perform the Cox-regression fashizzle and plot it...\n"))
    ### Do Cox-regression and plot it
    
    ### MODEL 2 adjusted for age, sex, hypertension, diabetes, smoking, LDL-C levels, lipid-lowering drugs, antiplatelet drugs, eGFR, BMI, history of CVD, level of stenosis
    cox = coxph(Surv(TEMP.DF[,eptime], event) ~ TEMP.DF[[ TRAITS.TARGET.RANK[target_of_interest] ]]+Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus + SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + MedHx_CVD + stenose, data = TEMP.DF)
    coxplot = coxph(Surv(TEMP.DF[,eptime], event) ~ strata(TEMP.DF[[ TRAITS.TARGET.RANK[target_of_interest] ]])+Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus + SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + MedHx_CVD + stenose, data = TEMP.DF)

  
    plot(survfit(coxplot), main = paste0("Cox proportional hazard of [",ep,"] per [",eptime,"]."),
         ylim = c(0.75, 1), xlim = c(0,3), col = c("#DB003F", "#1290D9"),
         # ylim = c(0, 1), xlim = c(0,3), col = c("#DB003F", "#1290D9"),
         lty = c(1,2), lwd = 2,
         ylab = "Suvival probability", xlab = "FU time [days]",
         mark.time = FALSE, axes = FALSE, bty = "n")
    legend("topright",
           c("low", "high"),
           title = paste0("",TRAITS.TARGET.RANK[target_of_interest],""),
           col = c("#DB003F", "#1290D9"),
           lty = c(1,2), lwd = 2,
           bty = "n")
    axis(side = 1, at = seq(0, 3, by = 1))
    axis(side = 2, at = seq(0, 1, by = 0.2))
    dev.copy2pdf(file = paste0(COX_loc,"/",
                               Today,".AERNASE.clin.hdac9.Cox.",ep,".2G.",
                               # Today,".AERNASE.clin.hdac9.Cox.",ep,".4G.",
                               TRAITS.TARGET.RANK[target_of_interest],".MODEL2.90days.pdf"), height = 12, width = 10, onefile = TRUE)

    show(summary(cox))

    cat(paste0("\n   > writing the Cox-regression fashizzle to Excel...\n"))

    COX.results.TEMP <- data.frame(matrix(NA, ncol = 12, nrow = 0))
    COX.results.TEMP[1,] = COX.STAT(cox, "AERNASE.clin.hdac9", ep, TRAITS.TARGET.RANK[target_of_interest])
    COX.results = rbind(COX.results, COX.results.TEMP)

  }
}

cat("- Edit the column names...\n")
colnames(COX.results) = c("Dataset", "Outcome", "CpG",
                          "Beta", "s.e.m.",
                          "HR", "low95CI", "up95CI",
                          "Z-value", "P-value", "SampleSize", "N_events")

cat("- Correct the variable types...\n")
COX.results$Beta <- as.numeric(COX.results$Beta)
COX.results$s.e.m. <- as.numeric(COX.results$s.e.m.)
COX.results$HR <- as.numeric(COX.results$HR)
COX.results$low95CI <- as.numeric(COX.results$low95CI)
COX.results$up95CI <- as.numeric(COX.results$up95CI)
COX.results$`Z-value` <- as.numeric(COX.results$`Z-value`)
COX.results$`P-value` <- as.numeric(COX.results$`P-value`)
COX.results$SampleSize <- as.numeric(COX.results$SampleSize)
COX.results$N_events <- as.numeric(COX.results$N_events)

AERNASE.clin.hdac9.COX.results <- COX.results

# Save the data
cat("- Writing results to Excel-file...\n")
head.style <- createStyle(textDecoration = "BOLD")
write.xlsx(AERNASE.clin.hdac9.COX.results,
           file = paste0(OUT_loc, "/",Today,".AERNASE.clin.hdac9.Cox.2G.MODEL2.90days.xlsx"),
           creator = "Sander W. van der Laan",
           sheetName = "Results", headerStyle = head.style,
           rowNames = FALSE, colNames = TRUE, overwrite = TRUE)

# Removing intermediates
cat("- Removing intermediate files...\n")
rm(TEMP.DF, target_of_interest, fit, cox, coxplot, COX.results, COX.results.TEMP, head.style, AERNASE.clin.hdac9.COX.results)


```


# Correlations
We correlated plaque levels of the biomarkers.

## Plaque `r TRAIT_OF_INTEREST` expression levels

```{r}

# Installation of ggcorrplot()
# --------------------------------
# if(!require(devtools)) 
#   install.packages("devtools")
# devtools::install_github("kassambara/ggcorrplot")

library(ggcorrplot)

```


```{r}

# Creating matrix - inverse-rank transformation
# --------------------------------
# AERNASE.clin.hdac9.temp <- subset(AERNASE.clin.hdac9, 
#                           select = c("IL6_rank", "MCP1_rank", "IL6_pg_ug_2015_rank", "MCP1_pg_ug_2015_rank", "IL6R_pg_ug_2015_rank",
#                                                TRAITS.BIN, TRAITS.CON.RANK)
#                                     )
# AERNASE.clin.hdac9.temp <- subset(AERNASE.clin.hdac9, 
#                           select = c("MCP1_rank", "MCP1_pg_ug_2015_rank",
#                                                TRAITS.BIN, TRAITS.CON.RANK)
#                                     )
AERNASE.clin.hdac9.temp <- subset(AERNASE.clin.hdac9, 
                          select = c("HDAC9",
                                     TRAITS.BIN, 
                                     TRAITS.CON.RANK,
                                     "Symptoms.5G", "AsymptSympt", "EP_major", "EP_composite")
                                    )


AERNASE.clin.hdac9.temp$CalcificationPlaque <- as.numeric(AERNASE.clin.hdac9.temp$CalcificationPlaque)
AERNASE.clin.hdac9.temp$CollagenPlaque <- as.numeric(AERNASE.clin.hdac9.temp$CollagenPlaque)
AERNASE.clin.hdac9.temp$Fat10Perc <- as.numeric(AERNASE.clin.hdac9.temp$Fat10Perc)
AERNASE.clin.hdac9.temp$MAC_binned <- as.numeric(AERNASE.clin.hdac9.temp$MAC_binned)
AERNASE.clin.hdac9.temp$SMC_binned <- as.numeric(AERNASE.clin.hdac9.temp$SMC_binned)
AERNASE.clin.hdac9.temp$IPH <- as.numeric(AERNASE.clin.hdac9.temp$IPH)
AERNASE.clin.hdac9.temp$Symptoms.5G <- as.numeric(AERNASE.clin.hdac9.temp$Symptoms.5G)
AERNASE.clin.hdac9.temp$AsymptSympt <- as.numeric(AERNASE.clin.hdac9.temp$AsymptSympt)
AERNASE.clin.hdac9.temp$EP_major <- as.numeric(AERNASE.clin.hdac9.temp$EP_major)
AERNASE.clin.hdac9.temp$EP_composite <- as.numeric(AERNASE.clin.hdac9.temp$EP_composite)
str(AERNASE.clin.hdac9.temp)
AERNASE.clin.hdac9.matrix.RANK <- as.matrix(AERNASE.clin.hdac9.temp)
rm(AERNASE.clin.hdac9.temp)

```

```{r CrossSampleType Correlations}
corr_biomarkers.rank <- round(cor(AERNASE.clin.hdac9.matrix.RANK, 
                             use = "pairwise.complete.obs", #the correlation or covariance between each pair of variables is computed using all complete pairs of observations on those variables
                             method = "spearman"), 3)
corr_biomarkers.rank

corr_biomarkers_p.rank <- ggcorrplot::cor_pmat(AERNASE.clin.hdac9.matrix.RANK, use = "pairwise.complete.obs", method = "spearman", exact = FALSE)
```

```{r}
# Add correlation coefficients
# --------------------------------
# argument lab = TRUE
ggcorrplot(corr_biomarkers.rank, 
           method = "square", 
           type = "lower",
           title = "Cross biomarker correlations", 
           show.legend = TRUE, legend.title = bquote("Spearman's"~italic(rho)),
           ggtheme = ggplot2::theme_minimal, outline.color = "#FFFFFF",
           show.diag = TRUE,
           hc.order = FALSE, 
           lab = FALSE,
           digits = 3,
           # p.mat = corr_biomarkers_p.rank, sig.level = 0.05,
           colors = c("#1290D9", "#FFFFFF", "#E55738"))

```

```{r Correlations table}
library(data.table)
# flattenCorrMatrix
# --------------------------------
# cormat : matrix of the correlation coefficients
# pmat : matrix of the correlation p-values
flattenCorrMatrix <- function(cormat, pmat) {
  ut <- upper.tri(cormat)
  data.frame(
    biomarker_row = rownames(cormat)[row(cormat)[ut]],
    biomarker_column = rownames(cormat)[col(cormat)[ut]],
    spearman_cor  =(cormat)[ut],
    pval = pmat[ut]
    )
}

corr_biomarkers.rank.df <- as.data.table(flattenCorrMatrix(corr_biomarkers.rank, corr_biomarkers_p.rank))
DT::datatable(corr_biomarkers.rank.df)

```

```{r Correlations alternative visual 1, message=FALSE, warning=FALSE}
# chart of a correlation matrix
# --------------------------------
# Alternative solution https://www.r-graph-gallery.com/199-correlation-matrix-with-ggally.html
install.packages.auto("PerformanceAnalytics")
chart.Correlation.new <- function (R, histogram = TRUE, method = c("pearson", "kendall", 
    "spearman"), ...) 
{
    x = checkData(R, method = "matrix")
    if (missing(method)) 
        method = method[1]
    cormeth <- method
    panel.cor <- function(x, y, digits = 2, prefix = "", use = "pairwise.complete.obs", 
        method = cormeth, cex.cor, ...) {
        usr <- par("usr")
        on.exit(par(usr))
        par(usr = c(0, 1, 0, 1))
        r <- cor(x, y, use = use, method = method)
        txt <- format(c(r, 0.123456789), digits = digits)[1]
        txt <- paste(prefix, txt, sep = "")
        if (missing(cex.cor)) 
            cex <- 0.8/strwidth(txt)
        test <- cor.test(as.numeric(x), as.numeric(y), method = method)
        Signif <- symnum(test$p.value, corr = FALSE, na = FALSE, 
            cutpoints = c(0, 0.001, 0.01, 0.05, 0.1, 1), symbols = c("***", 
                "**", "*", ".", " "))
        text(0.5, 0.5, txt, cex = cex * (abs(r) + 0.3)/1.3)
        text(0.8, 0.8, Signif, cex = cex, col = 2)
    }
    f <- function(t) {
        dnorm(t, mean = mean(x), sd = sd.xts(x))
    }
    dotargs <- list(...)
    dotargs$method <- NULL
    rm(method)
    hist.panel = function(x, ... = NULL) {
        par(new = TRUE)
        hist(x, col = "#1290D9", probability = TRUE, axes = FALSE, 
        # hist(x, col = "light gray", probability = TRUE, axes = FALSE, 
            main = "", breaks = "FD")
        lines(density(x, na.rm = TRUE), col = "#E55738", lwd = 1)
        rug(x)
    }
    if (histogram) 
        pairs(x, gap = 0, lower.panel = panel.smooth, upper.panel = panel.cor, 
            diag.panel = hist.panel, ...)
    else pairs(x, gap = 0, lower.panel = panel.smooth, upper.panel = panel.cor, ...)
}


chart.Correlation.new(AERNASE.clin.hdac9.matrix.RANK, method = "spearman", histogram = TRUE, pch = 3)
```


```{r Correlations alternative visual 2, message=FALSE, warning=FALSE}
# alternative chart of a correlation matrix
# --------------------------------
# Alternative solution https://www.r-graph-gallery.com/199-correlation-matrix-with-ggally.html
install.packages.auto("GGally")

# Quick display of two cabapilities of GGally, to assess the distribution and correlation of variables 
library(GGally)
 
# From the help page:

ggpairs(AERNASE.clin.hdac9,
        columns = c("HDAC9", TRAITS.BIN, TRAITS.CON.RANK, "Symptoms.5G", "AsymptSympt", "EP_major", "EP_composite"),
        columnLabels = c("HDAC9",
                         "Calcification", "Collagen", "Fat 10%", "IPH", "Macrophages (binned)", "SMC (binned)", "Macrophages", "SMC", "Macrophage/SMC", "Vessel density",
                         "Symptoms", "Symptoms (grouped)", "MACE", "Composite"),
        method = c("spearman"),
        # ggplot2::aes(colour = Gender),
        progress = FALSE)

```
# Saving datasets

For the purpose of downstream analyses we save the `AEDB.CEA` object, and the _HDAC9_ objects.

```{r}

saveRDS(AEDB.CEA, file = paste0(OUT_loc, "/", Today, ".",TRAIT_OF_INTEREST,".AEDB.CEA.withupdates.RDS"))
saveRDS(AEDB.CEA, file = paste0(OUT_loc, "/", Today, ".",TRAIT_OF_INTEREST,".AERNASE.clin.hdac9.df.RDS"))
saveRDS(AEDB.CEA, file = paste0(OUT_loc, "/", Today, ".",TRAIT_OF_INTEREST,".AERNASE.hdac9.se.RDS"))
saveRDS(AEDB.CEA, file = paste0(OUT_loc, "/", Today, ".",TRAIT_OF_INTEREST,".AERNASE.hdac9.genedata.RDS"))

```


# Session information

--------------------------------------------------------------------------------

    Version:      v1.0.1
    Last update:  2021-03-19
    Written by:   Sander W. van der Laan (s.w.vanderlaan-2[at]umcutrecht.nl).
    Description:  Script to analyse HDAC9 from the Ather-Express Biobank Study.
    Minimum requirements: R version 3.5.2 (2018-12-20) -- 'Eggshell Igloo', macOS Mojave (10.14.2).
    
    **MoSCoW To-Do List**
    The things we Must, Should, Could, and Would have given the time we have.
    _M_

    _S_

    _C_

    _W_

    **Changes log**
    * v1.0.1 Update to main AEDB (there is an error in the Age-variable in the new version). More patients in the bulk (623 vs 611 with the newer dataset).
    * v1.0.0 Inital version.
    

--------------------------------------------------------------------------------

```{r eval = TRUE}
sessionInfo()
```

# Saving environment
```{r Saving}
save.image(paste0(PROJECT_loc, "/",Today,".",PROJECTNAME,".bulkRNAseq.main_analysis.RData"))
```

+-----------------------------------------------------------------------------------------------------------------------------------------+
| <sup>© 1979-2022 Sander W. van der Laan | s.w.vanderlaan[at]gmail.com | [swvanderlaan.github.io](https://swvanderlaan.github.io).</sup> |
+-----------------------------------------------------------------------------------------------------------------------------------------+


